{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "genomics_simulation.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kundajelab/deeplift/blob/master/examples/genomics/genomics_simulation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcyLiGtl8N2S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "905a0cdd-712d-4b03-d0a2-c5b11c84b9b9"
      },
      "source": [
        "#Use an older version of hdf5 for backwards compatibility with when\n",
        "# this model was created\n",
        "!pip uninstall -y h5py\n",
        "!pip install h5py==2.10.0\n",
        "\n",
        "%tensorflow_version 1.x #select tf 1.x in Google Colab"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: h5py 3.1.0\n",
            "Uninstalling h5py-3.1.0:\n",
            "  Successfully uninstalled h5py-3.1.0\n",
            "Collecting h5py==2.10.0\n",
            "  Downloading h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9 MB 8.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.7/dist-packages (from h5py==2.10.0) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from h5py==2.10.0) (1.15.0)\n",
            "Installing collected packages: h5py\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.6.0 requires h5py~=3.1.0, but you have h5py 2.10.0 which is incompatible.\u001b[0m\n",
            "Successfully installed h5py-2.10.0\n",
            "`%tensorflow_version` only switches the major version: 1.x or 2.x.\n",
            "You set: `1.x #select tf 1.x in Google Colab`. This will be interpreted as: `1.x`.\n",
            "\n",
            "\n",
            "TensorFlow 1.x selected.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfWFpUho8MWN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db2d3166-995a-416d-d6a4-2f9f0eb379fd"
      },
      "source": [
        "from __future__ import print_function\n",
        "import tensorflow\n",
        "print(\"Tensorflow version:\", tensorflow.__version__)\n",
        "import keras\n",
        "print(\"Keras version:\", keras.__version__)\n",
        "import numpy\n",
        "print(\"Numpy version:\", numpy.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensorflow version: 1.15.2\n",
            "Keras version: 2.3.1\n",
            "Numpy version: 1.19.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using TensorFlow backend.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isQeyq008UDP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb40d0a4-e887-4aeb-f0bd-a9e350b18ca1"
      },
      "source": [
        "!pip install deeplift\n",
        "!pip install simdna==0.4.3.2"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting deeplift\n",
            "  Downloading deeplift-0.6.13.0.tar.gz (30 kB)\n",
            "Requirement already satisfied: numpy>=1.9 in /usr/local/lib/python3.7/dist-packages (from deeplift) (1.19.5)\n",
            "Building wheels for collected packages: deeplift\n",
            "  Building wheel for deeplift (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for deeplift: filename=deeplift-0.6.13.0-py3-none-any.whl size=36449 sha256=91960f086e0405d48b94a3f9c59ddc332cbe664513a82b6fdc53f2165c0236cb\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/0f/15/3cb70dbd2147a5c948da210c6a283d87ea19c8e8ecb941b21e\n",
            "Successfully built deeplift\n",
            "Installing collected packages: deeplift\n",
            "Successfully installed deeplift-0.6.13.0\n",
            "Collecting simdna==0.4.3.2\n",
            "  Downloading simdna-0.4.3.2.tar.gz (634 kB)\n",
            "\u001b[K     |████████████████████████████████| 634 kB 8.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.9 in /usr/local/lib/python3.7/dist-packages (from simdna==0.4.3.2) (1.19.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from simdna==0.4.3.2) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from simdna==0.4.3.2) (1.4.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->simdna==0.4.3.2) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->simdna==0.4.3.2) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->simdna==0.4.3.2) (1.3.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->simdna==0.4.3.2) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->simdna==0.4.3.2) (1.15.0)\n",
            "Building wheels for collected packages: simdna\n",
            "  Building wheel for simdna (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for simdna\u001b[0m\n",
            "\u001b[?25h  Running setup.py clean for simdna\n",
            "Failed to build simdna\n",
            "Installing collected packages: simdna\n",
            "    Running setup.py install for simdna ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33m  DEPRECATION: simdna was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. A possible replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/issues/8368.\u001b[0m\n",
            "Successfully installed simdna-0.4.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-L4hTSr8MWS"
      },
      "source": [
        "## Genomics example\n",
        "\n",
        "This will explore how importance scores from three different methods compare on simulated genomic data.\n",
        "\n",
        "The simulated data was as follows:\n",
        "\n",
        "- 1/4 sequences with 1-3 instances of a GATA_disc1 motif embedded (see http://compbio.mit.edu/encode-motifs/ for the PWM); these were labelled 1,0,0\n",
        "- 1/4 sequences with 1-3 instances of a TAL1_known1 motif embedded; these were labelled 0,1,0\n",
        "- 1/4 sequences with BOTH 1-3 instances of a GATA_disc1 motif AND 1-3 instances of a TAL1_known1 motif; these were labelled 1,1,1\n",
        "- 1/4 sequences with no motif\n",
        "\n",
        "Scores for all three tasks for sequences that contain both TAL1_known1 and GATA_disc1 motifs are analyzed in this notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-VMV6sh8MWT"
      },
      "source": [
        "## Obtain data and keras model\n",
        "We will download genomic data and model\n",
        "\n",
        "### Download the data and model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnbNxDXW8MWT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d2db003-a040-48c4-f3e7-99afbed26a91"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/kundajelab/deeplift/d95d41d/examples/genomics/grab_model_and_data.sh\n",
        "!chmod a+x grab_model_and_data.sh\n",
        "!./grab_model_and_data.sh"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-10-20 09:33:51--  https://raw.githubusercontent.com/kundajelab/deeplift/d95d41d/examples/genomics/grab_model_and_data.sh\n",
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            "Saving to: ‘grab_model_and_data.sh’\n",
            "\n",
            "grab_model_and_data 100%[===================>]   1.12K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-10-20 09:33:51 (45.8 MB/s) - ‘grab_model_and_data.sh’ saved [1145/1145]\n",
            "\n",
            "--2021-10-20 09:33:51--  https://raw.githubusercontent.com/AvantiShri/model_storage/db919b12f750e5844402153233249bb3d24e9e9a/deeplift/genomics/sequences.simdata.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
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            "Saving to: ‘sequences.simdata.gz’\n",
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            "\n",
            "2021-10-20 09:33:52 (20.6 MB/s) - ‘sequences.simdata.gz’ saved [629502/629502]\n",
            "\n",
            "--2021-10-20 09:33:52--  https://raw.githubusercontent.com/AvantiShri/model_storage/b6e1d69/deeplift/genomics/keras2_conv1d_record_5_model_PQzyq_modelJson.json\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
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            "Saving to: ‘keras2_conv1d_record_5_model_PQzyq_modelJson.json’\n",
            "\n",
            "keras2_conv1d_recor 100%[===================>]   2.94K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-10-20 09:33:53 (6.94 MB/s) - ‘keras2_conv1d_record_5_model_PQzyq_modelJson.json’ saved [3010/3010]\n",
            "\n",
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            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
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            "\n",
            "2021-10-20 09:33:53 (10.3 MB/s) - ‘keras2_conv1d_record_5_model_PQzyq_modelWeights.h5’ saved [153864/153864]\n",
            "\n",
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            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6h7Wc3z8MWX"
      },
      "source": [
        "### Read in and one-hot encode the data\n",
        "\n",
        "The simdna package is needed for reading the data; install it if it doesn't exist"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHTZMlFG8MWa"
      },
      "source": [
        "Read in data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOA2KKLv8MWb"
      },
      "source": [
        "import simdna.synthetic as synthetic\n",
        "import gzip\n",
        "data_filename = \"sequences.simdata.gz\"\n",
        "\n",
        "#read in the data in the testing set\n",
        "test_ids_fh = gzip.open(\"test.txt.gz\",\"rb\")\n",
        "ids_to_load = [x.decode(\"utf-8\").rstrip(\"\\n\") for x in test_ids_fh]\n",
        "data = synthetic.read_simdata_file(data_filename, ids_to_load=ids_to_load)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JznAYsZS8MWd"
      },
      "source": [
        "One-hot encode the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fs2z4iKS8MWe"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "#this is set up for 1d convolutions where examples\n",
        "#have dimensions (len, num_channels)\n",
        "#the channel axis is the axis for one-hot encoding.\n",
        "def one_hot_encode_along_channel_axis(sequence):\n",
        "    to_return = np.zeros((len(sequence),4), dtype=np.int8)\n",
        "    seq_to_one_hot_fill_in_array(zeros_array=to_return,\n",
        "                                 sequence=sequence, one_hot_axis=1)\n",
        "    return to_return\n",
        "\n",
        "def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis):\n",
        "    assert one_hot_axis==0 or one_hot_axis==1\n",
        "    if (one_hot_axis==0):\n",
        "        assert zeros_array.shape[1] == len(sequence)\n",
        "    elif (one_hot_axis==1): \n",
        "        assert zeros_array.shape[0] == len(sequence)\n",
        "    #will mutate zeros_array\n",
        "    for (i,char) in enumerate(sequence):\n",
        "        if (char==\"A\" or char==\"a\"):\n",
        "            char_idx = 0\n",
        "        elif (char==\"C\" or char==\"c\"):\n",
        "            char_idx = 1\n",
        "        elif (char==\"G\" or char==\"g\"):\n",
        "            char_idx = 2\n",
        "        elif (char==\"T\" or char==\"t\"):\n",
        "            char_idx = 3\n",
        "        elif (char==\"N\" or char==\"n\"):\n",
        "            continue #leave that pos as all 0's\n",
        "        else:\n",
        "            raise RuntimeError(\"Unsupported character: \"+str(char))\n",
        "        if (one_hot_axis==0):\n",
        "            zeros_array[char_idx,i] = 1\n",
        "        elif (one_hot_axis==1):\n",
        "            zeros_array[i,char_idx] = 1\n",
        "            \n",
        "onehot_data = np.array([one_hot_encode_along_channel_axis(seq) for seq in data.sequences])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-izUsr8B8MWg"
      },
      "source": [
        "### Load the keras model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6RzQLT-8MWh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf75bcc9-e06e-46a5-fb75-767ffde835b8"
      },
      "source": [
        "import deeplift\n",
        "from keras.models import model_from_json\n",
        "\n",
        "#load the keras model\n",
        "keras_model_weights = \"keras2_conv1d_record_5_model_PQzyq_modelWeights.h5\"\n",
        "keras_model_json = \"keras2_conv1d_record_5_model_PQzyq_modelJson.json\"\n",
        "\n",
        "keras_model = model_from_json(open(keras_model_json).read())\n",
        "keras_model.load_weights(keras_model_weights)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/keras/backend/tensorflow_backend.py:4074: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8tU1Lls8MWk"
      },
      "source": [
        "## Prepare the deeplift models\n",
        "\n",
        "### Model conversion\n",
        "\n",
        "Convert the keras models to deeplift models capable of computing importance scores using DeepLIFT (with 3 different variants: rescale on the conv layers and revealcancel on the fully-connected layers (the genomics default), rescale on all layers, and revealcancel on all layers), gradients and guided backprop\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SE57eApN8MWk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc5e8fd3-db65-4733-bd8f-b9d117c0e9c9"
      },
      "source": [
        "from deeplift.layers import NonlinearMxtsMode\n",
        "import deeplift.conversion.kerasapi_conversion as kc\n",
        "from collections import OrderedDict\n",
        "\n",
        "method_to_model = OrderedDict()\n",
        "for method_name, nonlinear_mxts_mode in [\n",
        "    #The genomics default = rescale on conv layers, revealcance on fully-connected\n",
        "    ('rescale_conv_revealcancel_fc', NonlinearMxtsMode.DeepLIFT_GenomicsDefault),\n",
        "    ('rescale_all_layers', NonlinearMxtsMode.Rescale),\n",
        "    ('revealcancel_all_layers', NonlinearMxtsMode.RevealCancel),\n",
        "    ('grad_times_inp', NonlinearMxtsMode.Gradient),\n",
        "    ('guided_backprop', NonlinearMxtsMode.GuidedBackprop)]:\n",
        "    method_to_model[method_name] = kc.convert_model_from_saved_files(\n",
        "        h5_file=keras_model_weights,\n",
        "        json_file=keras_model_json,\n",
        "        nonlinear_mxts_mode=nonlinear_mxts_mode)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nonlinear_mxts_mode is set to: DeepLIFT_GenomicsDefault\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/deeplift/layers/core.py:207: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/deeplift/conversion/kerasapi_conversion.py:366: H5pyDeprecationWarning: The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details.\n",
            "  model_weights = h5py.File(h5_file)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For layer 1 the preceding linear layer is 0 of type Conv1D;\n",
            "In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
            "For layer 3 the preceding linear layer is 2 of type Conv1D;\n",
            "In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
            "For layer 7 the preceding linear layer is 6 of type Dense;\n",
            "In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n",
            "For layer 10 the preceding linear layer is 9 of type Dense;\n",
            "In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n",
            "nonlinear_mxts_mode is set to: Rescale\n",
            "nonlinear_mxts_mode is set to: RevealCancel\n",
            "nonlinear_mxts_mode is set to: Gradient\n",
            "nonlinear_mxts_mode is set to: GuidedBackprop\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "PS9fKrXI8MWo"
      },
      "source": [
        "### Sanity checks\n",
        "To ensure that the conversion happend correctly, ensure that the models give identical predictions\n",
        "\n",
        "If you are using a functional model, see this issue for how to adapt the code: https://github.com/kundajelab/deeplift/issues/54"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIY7n62Y8MWo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c22f2c0d-19fb-4ad6-cae9-9db826893b47"
      },
      "source": [
        "#make sure predictions are the same as the original model\n",
        "from deeplift.util import compile_func\n",
        "model_to_test = method_to_model['rescale_conv_revealcancel_fc']\n",
        "deeplift_prediction_func = compile_func([model_to_test.get_layers()[0].get_activation_vars()],\n",
        "                                         model_to_test.get_layers()[-1].get_activation_vars())\n",
        "original_model_predictions = keras_model.predict(onehot_data, batch_size=200)\n",
        "converted_model_predictions = deeplift.util.run_function_in_batches(\n",
        "                                input_data_list=[onehot_data],\n",
        "                                func=deeplift_prediction_func,\n",
        "                                batch_size=200,\n",
        "                                progress_update=None)\n",
        "print(\"maximum difference in predictions:\",np.max(np.array(converted_model_predictions)-np.array(original_model_predictions)))\n",
        "assert np.max(np.array(converted_model_predictions)-np.array(original_model_predictions)) < 10**-5\n",
        "predictions = converted_model_predictions"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "maximum difference in predictions: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8f5zmx_8MWq"
      },
      "source": [
        "## Compute importance scores\n",
        "\n",
        "### Compile various scoring functions\n",
        "Using the deeplift models, we obtain the functions capable of computing the importance scores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15wD2vUg8MWr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "785a7661-0486-4a23-e0bd-8ee3902eaa77"
      },
      "source": [
        "print(\"Compiling scoring functions\")\n",
        "method_to_scoring_func = OrderedDict()\n",
        "for method,model in method_to_model.items():\n",
        "    print(\"Compiling scoring function for: \"+method)\n",
        "    method_to_scoring_func[method] = model.get_target_contribs_func(find_scores_layer_idx=0,\n",
        "                                                                    target_layer_idx=-2)\n",
        "    \n",
        "#To get a function that just gives the gradients, we use the multipliers of the Gradient model\n",
        "gradient_func = method_to_model['grad_times_inp'].get_target_multipliers_func(find_scores_layer_idx=0,\n",
        "                                                                              target_layer_idx=-2)\n",
        "print(\"Compiling integrated gradients scoring functions\")\n",
        "integrated_gradients10_func = deeplift.util.get_integrated_gradients_function(\n",
        "    gradient_computation_function = gradient_func,\n",
        "    num_intervals=10)\n",
        "method_to_scoring_func['integrated_gradients10'] = integrated_gradients10_func"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Compiling scoring functions\n",
            "Compiling scoring function for: rescale_conv_revealcancel_fc\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/deeplift/layers/core.py:455: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/deeplift/layers/core.py:471: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/deeplift/layers/core.py:473: The name tf.scatter_update is deprecated. Please use tf.compat.v1.scatter_update instead.\n",
            "\n",
            "Compiling scoring function for: rescale_all_layers\n",
            "Compiling scoring function for: revealcancel_all_layers\n",
            "Compiling scoring function for: grad_times_inp\n",
            "Compiling scoring function for: guided_backprop\n",
            "Compiling integrated gradients scoring functions\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJVPrHXt8MWt"
      },
      "source": [
        "### Call scoring functions on the data\n",
        "\n",
        "In the cell below, a reference representing 40% GC content is used"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdFKNZI88MWt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6565c57-5b94-4b15-8964-f62be4cee6ee"
      },
      "source": [
        "background = OrderedDict([('A', 0.3), ('C', 0.2), ('G', 0.2), ('T', 0.3)])\n",
        "\n",
        "from collections import OrderedDict\n",
        "method_to_task_to_scores = OrderedDict()\n",
        "for method_name, score_func in method_to_scoring_func.items():\n",
        "    print(\"on method\",method_name)\n",
        "    method_to_task_to_scores[method_name] = OrderedDict()\n",
        "    for task_idx in [0,1,2]:\n",
        "        scores = np.array(score_func(\n",
        "                    task_idx=task_idx,\n",
        "                    input_data_list=[onehot_data],\n",
        "                    input_references_list=[\n",
        "                     np.array([background['A'],\n",
        "                               background['C'],\n",
        "                               background['G'],\n",
        "                               background['T']])[None,None,:]],\n",
        "                    batch_size=200,\n",
        "                    progress_update=None))\n",
        "        assert scores.shape[2]==4\n",
        "        #The sum over the ACGT axis in the code below is important! Recall that DeepLIFT\n",
        "        # assigns contributions based on difference-from-reference; if\n",
        "        # a position is [1,0,0,0] (i.e. 'A') in the actual sequence and [0.3, 0.2, 0.2, 0.3]\n",
        "        # in the reference, importance will be assigned to the difference (1-0.3)\n",
        "        # in the 'A' channel, (0-0.2) in the 'C' channel,\n",
        "        # (0-0.2) in the G channel, and (0-0.3) in the T channel. You want to take the importance\n",
        "        # on all four channels and sum them up, so that at visualization-time you can project the\n",
        "        # total importance over all four channels onto the base that is actually present (i.e. the 'A'). If you\n",
        "        # don't do this, your visualization will look very confusing as multiple bases will be highlighted at\n",
        "        # every position and you won't know which base is the one that is actually present in the sequence!\n",
        "        scores = np.sum(scores, axis=2)\n",
        "        method_to_task_to_scores[method_name][task_idx] = scores"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "on method rescale_conv_revealcancel_fc\n",
            "on method rescale_all_layers\n",
            "on method revealcancel_all_layers\n",
            "on method grad_times_inp\n",
            "on method guided_backprop\n",
            "on method integrated_gradients10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGLI-S4Z8MWx"
      },
      "source": [
        "## Using multiple shuffled references\n",
        "\n",
        "As an alternative to using a flat reference based on GC content (which can sometimes produce artefacts), we propose averaging the scores produced using mutliple references which are produced by shuffling the original sequence. We find in practice that this can give more robust results. Note that in general, the optimal choice of reference is an area of active research."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "ahGhvdWL8MWx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7496a364-9af7-4813-b7b0-f4e844ece0ad"
      },
      "source": [
        "from deeplift.util import get_shuffle_seq_ref_function\n",
        "#from deeplift.util import randomly_shuffle_seq\n",
        "from deeplift.dinuc_shuffle import dinuc_shuffle #function to do a dinucleotide shuffle\n",
        "\n",
        "rescale_conv_revealcancel_fc_many_refs_func = get_shuffle_seq_ref_function(\n",
        "    #score_computation_function is the original function to compute scores\n",
        "    score_computation_function=method_to_scoring_func['rescale_conv_revealcancel_fc'],\n",
        "    #shuffle_func is the function that shuffles the sequence\n",
        "    #technically, given the background of this simulation, randomly_shuffle_seq\n",
        "    #makes more sense. However, on real data, a dinuc shuffle is advisable due to\n",
        "    #the strong bias against CG dinucleotides\n",
        "    shuffle_func=dinuc_shuffle,\n",
        "    one_hot_func=lambda x: np.array([one_hot_encode_along_channel_axis(seq) for seq in x]))\n",
        "\n",
        "num_refs_per_seq=20 #number of references to generate per sequence\n",
        "method_to_task_to_scores['rescale_conv_revealcancel_fc_multiref_'+str(num_refs_per_seq)] = OrderedDict()\n",
        "scores_without_sum_applied = rescale_conv_revealcancel_fc_many_refs_func(\n",
        "            task_idx=[0,1,2], #can provide a list of tasks; references will be reused for each\n",
        "            #Providing a single integeter for task_idx works too and would return a numpy array rather\n",
        "            # than a list of numpy arrays\n",
        "            input_data_sequences=data.sequences,\n",
        "            num_refs_per_seq=num_refs_per_seq,\n",
        "            batch_size=200,\n",
        "            progress_update=1000)\n",
        "for task_idx, scores in enumerate(scores_without_sum_applied):\n",
        "    #The sum over the ACGT axis in the code below is important! Recall that DeepLIFT\n",
        "    # assigns contributions based on difference-from-reference; if\n",
        "    # a position is [1,0,0,0] (i.e. 'A') in the actual sequence and [0, 1, 0, 0]\n",
        "    # in the reference, importance will be assigned to the difference (1-0)\n",
        "    # in the 'A' channel, and (0-1) in the 'C' channel. You want to take the importance\n",
        "    # on all channels and sum them up, so that at visualization-time you can project the\n",
        "    # total importance over all four channels onto the base that is actually present (i.e. the 'A'). If you\n",
        "    # don't do this, your visualization will look very confusing as multiple bases will be highlighted at\n",
        "    # every position and you won't know which base is the one that is actually present in the sequence!\n",
        "    method_to_task_to_scores['rescale_conv_revealcancel_fc_multiref_'+str(num_refs_per_seq)][task_idx] =\\\n",
        "        np.sum(scores,axis=2)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000 reference seqs generated\n",
            "2000 reference seqs generated\n",
            "3000 reference seqs generated\n",
            "4000 reference seqs generated\n",
            "5000 reference seqs generated\n",
            "6000 reference seqs generated\n",
            "7000 reference seqs generated\n",
            "8000 reference seqs generated\n",
            "9000 reference seqs generated\n",
            "10000 reference seqs generated\n",
            "11000 reference seqs generated\n",
            "12000 reference seqs generated\n",
            "13000 reference seqs generated\n",
            "14000 reference seqs generated\n",
            "15000 reference seqs generated\n",
            "16000 reference seqs generated\n",
            "One hot encoding sequences...\n",
            "One hot encoding done...\n",
            "Done 0\n",
            "Done 1000\n",
            "Done 2000\n",
            "Done 3000\n",
            "Done 4000\n",
            "Done 5000\n",
            "Done 6000\n",
            "Done 7000\n",
            "Done 8000\n",
            "Done 9000\n",
            "Done 10000\n",
            "Done 11000\n",
            "Done 12000\n",
            "Done 13000\n",
            "Done 14000\n",
            "Done 15000\n",
            "Done 0\n",
            "Done 1000\n",
            "Done 2000\n",
            "Done 3000\n",
            "Done 4000\n",
            "Done 5000\n",
            "Done 6000\n",
            "Done 7000\n",
            "Done 8000\n",
            "Done 9000\n",
            "Done 10000\n",
            "Done 11000\n",
            "Done 12000\n",
            "Done 13000\n",
            "Done 14000\n",
            "Done 15000\n",
            "Done 0\n",
            "Done 1000\n",
            "Done 2000\n",
            "Done 3000\n",
            "Done 4000\n",
            "Done 5000\n",
            "Done 6000\n",
            "Done 7000\n",
            "Done 8000\n",
            "Done 9000\n",
            "Done 10000\n",
            "Done 11000\n",
            "Done 12000\n",
            "Done 13000\n",
            "Done 14000\n",
            "Done 15000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DyPNclLM8MWz"
      },
      "source": [
        "## Visualize scores on individual sequences\n",
        "\n",
        "Visualize the scores at specific sequences. Cyan boxes indicate the ground-truth locations of the inserted TAL1_known1 motifs, red boxes indicate the ground-truth locations of the inserted GATA_disc1 motifs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "mD892Ip88MW0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d61bcd66-d75d-414c-c36e-8ae302442637"
      },
      "source": [
        "#visualize scores + ground-truth locations of motifs\n",
        "%matplotlib inline\n",
        "from deeplift.visualization import viz_sequence\n",
        "\n",
        "for task, idx in [(0,731), #illustrates failure of grad*inp, integrated grads, deeplift-rescale\n",
        "                  (1,197)  #illustrates non-specific firing of guided backprop\n",
        "                 ]:\n",
        "    print(\"Scores for task\",task,\"for example\",idx)\n",
        "    for method_name in [\n",
        "                        'grad_times_inp',\n",
        "                        'guided_backprop',\n",
        "                        'integrated_gradients10',\n",
        "                        'rescale_all_layers', 'revealcancel_all_layers',\n",
        "                        'rescale_conv_revealcancel_fc',\n",
        "                        'rescale_conv_revealcancel_fc_multiref_'+str(num_refs_per_seq)\n",
        "                        ]:\n",
        "        scores = method_to_task_to_scores[method_name][task]\n",
        "        scores_for_idx = scores[idx]\n",
        "        original_onehot = onehot_data[idx]\n",
        "        scores_for_idx = original_onehot*scores_for_idx[:,None]\n",
        "        print(method_name)\n",
        "        highlight = {'blue':[\n",
        "                (embedding.startPos, embedding.startPos+len(embedding.what))\n",
        "                for embedding in data.embeddings[idx] if 'GATA_disc1' in embedding.what.getDescription()],\n",
        "                'green':[\n",
        "                (embedding.startPos, embedding.startPos+len(embedding.what))\n",
        "                for embedding in data.embeddings[idx] if 'TAL1_known1' in embedding.what.getDescription()]}\n",
        "        viz_sequence.plot_weights(scores_for_idx, subticks_frequency=10, highlight=highlight)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for task 0 for example 731\n",
            "grad_times_inp\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gwRAR4UyAsmaNMyVv7UDWta2MvG4vu6rBlqYHuqhRNx7RzhrnpcHUjWmUX5FPiyKTobrQac4uIiIiQVdVBVFRzdOXpy8HoLSqFIOhxl9DScQAKN5V3wL4VMcadHnBX954DKNT7eImInKaKWgkItIGgwY1nj0XnKCRwdSNZxTmCWN3tYHiPfWzp11xCK6zlAVasM8fPJ+Pv/YxAKHHGssgfpwzdkLaPVBdUj+2kYiIiARFaCgUFTVPf2fPOwD4rA+Lc+D/tLQU8jc2PrafirAkKM0EGpxInGQXN5/PuVElInK6KWgkItIGAwfWD4Jdq7iyGGNM3cxpYZ4w9laDqSmFg2+Av74ZekagpdGghEEMTBgIwKHSY5x0RvUH/LD3n1C0zWl1JCIi0sls2QL/+x8sXgyrV5/Z4MiQIbByJdTUNE7fnbcbAJdx4Qq0Gt5cXgFl6VB+sHUrC090upwXbq1PO05rpZoa+PWvYcIEmDcPLrwQJk6EsrLWrV5E5GQEJWhkjJlrjNlhjNltjPlpC++HGmOeC7z/kTEmJRjrFRFpb5GR0K9f47SSqhIMpq57WpgnjBpclIX3gbS7wV/hZKwpY2dgTKPeMb2J8kbhdXspqy6jpKqk+cqMCxJSnbEPlkyCxd1OY81ERETOrH37YMoU+Oc/ITYWkpJg1SrIyztzZTj/fGcw7IqKxum1x+Xnr3qeh+Y+BMAOf7QzLtH670BNWevGJoodCbv+7LQghuO2VvriFyErywlqPf88LFoEy5Y53eRFRE6XNgeNjDFu4M/APGA48EVjzPAm2b4C5FtrBwIPAg+0db0iZ7uUFDCm8/6lpLT3J3z2uPzy+gEz3W4oqy7DZVx13dNqg0fpCec6Yx98eC3kroV132JHIGiUGOXcWYwPiwcgoyCj5ZX1vkwDYYuISKdQVl3G9H9M59GPHwXgc5+DBQvg7rth5kyYPh2++U3o2vXMlWn2bFi7Ftavr2/BU1oKm3M2AzCs2zCGdh0KwKqD6yHxfKcV8dpbYd9Tp77C5Gsh80Uo2uqMjVTTwk0j4MABWLoUfv97J0gUF+f8xcS0qpoiIictGC2NJgG7rbV7rbVVwLPAZU3yXAb8O/B8ETDHGKO5JaVTy8hwxrpp+nfkCHz4oXPgX7LEeVy1Co4ebZI3byP27fOwB17HVhzFVpdhK/OxNZX8YdWDsNDAQkPygykcLc3DJiZhMc3/EpNaLEezv1eHYbOXY6tL69Oqy7B+22L+jGPEND6L5s+vn5o3IgLKa8qdoFGge1qUNwqLZWv0JOfDy3oNlk6GvU+wr9pp6p4UlQRA14iuuIyL9IL0llfW70aaTc17qgNvioiItLP9hfvp/tvurDqwiq+/8XXueu8u9uyBUaOcVry1ws7wfZIxY5wyXHwx/Oc/Tve4Sy6t5FCJ0wIoJS6F/vH9MRh25u2EId8GdySkPwVrbjr1Ffb/EmDh7XPhkx/Aqi+1mC0szBnHqKrJrKs+TbwmIqeZ58RZTqgXsL/B6wPA5GPlsdbWGGMKgS5A7qmubNXTz9BncHdCQr11aQf35lCUnc7ASePwhITUpT/yxt/ZH5dHQmwCfmvx42eyHUPPfMPgKRPweOvzbnpvHT37J9G1b++6tM17d/Pk9qco7+ImNMRLtb+GvhE98e0v49mqt8n3OXcCEkJj+UmPm5hY7GPQuXMblMGybuVBwryWnsOHBa7zDMX5Jez/9CNGz5qAN6y+DIfWv0pSShJxA+o/vtK8o5R8+mcSZ/8clzcCZ6A8Q9oHm3HX5NB92GRMYMalktIyFi17hotnfo7usd3xWR/bCveSubMPlVmhjJucQFioBSw1PjfVWSshYQIxcaH4rcFlLOu2LyfMe4jpY87H4/KSW5HPjuJ9jCvIJM81g/Au/fDjwhhLZM02XGXplMfMooYIrAW3y0/I4VfxxY7GF9Yfi8Fgyc3IJLxmN33GjK4rr7Px3iSncjiuuGH4/G5cxk+4zcSUbKc6bhZ+VxTWgjGW5e+7GZhcRLceMbhMDWAw1fkU52QSmTiEkFAvfj+4jJ+Nn5TTt1cZsT164TJ+wJCd42Xlh6HMOqeM+NhqXMaPz+9i34Yt9O0XRZ8B9bdqcvNCeO31ECZMCSch1o+hBmu8ULKL3MIo+vbvgs+6MUBJKXy8porUSYbQsBBcLovP7wJG8tgTv2P22NG4MewvO8R/n+vGkf1zeemlxt/rPy/6HfPfvYe8GmeMGhcunu4/iqv7j8bV6xJnwOOX+0DFYb6xtwt/9R2t+9+Mwgy6/6Ybz3/7p4ybOYN+My52vk+bVrB606cUl2Qz55PluEM8FFQVs7dkPxs+XMOsiTPpnzQAn/WxoyidoYUhJJZk4O46GaqLYMv9sPXXPL36Znpe+jBR7gO4bCUWDzkHi4BpHPh0ZaMpaRcveZFRQ4czONm5A5deksXRA4UM/9di+r3xdqM6P3Hr5+k77wJGDBgFwP7SbI4cLCQ0K48hU1PxNPiNp63cQPfe8XTvl1yX5qo+SlXWCqIHnkeN6cqhHC/9k5225G88nUZCz0RC/DlYPPgJxZO3gt7D+lFeEUpxiZuE+BrWblpKl5hwBvVzfnP5VUVsP5RO+qaNzJs5n7joePzWz/aifaSnpZMQEcLksedgjCG/qohDJQcYUpTG4J6ziQy/hOJiL+ERJZT4fRgDxbs2cig0HU9VNf4aF0/+NYpB593FKHMXbluGz0Ry0FeFsX7cBw9wqGQlXQjHWj97/vsSey+E/rX7h80r2bozgg0bDFeNvZlevicwVFPhGcQnZT8nZf27jfZpn27bxtGj+5g1+UI8IR6q/TVsK9zDtjUbmDp6Mn17pQCQVXaYvZmhZGzoxahpg4mNLMbjL6DCM5Alb4fQs6dl/NAsPP58rAklPcfZR777Wgax4UUY48PiJfT+Oxn5zuJG2zg3tgsfXvktulx1LWHhYYG9F0SVvIMFXAljqah0YbBUVRu2byllyIhwvKFhGGPxWxf7Pt1CUp9w+g2Oq1tuUX4Ze9Z+xMiZ0wgJqx80/NUPlxIRbpgxZiZu42J/2SEycrOJ3lLMqNnT8IbXX3m4izaTvreMsORZGON813w1ft5/r5opUyzeiEhcxo/futiwJYYjR2Dm9BLCvM6+p7rGxfMvJzBjail9elbU5U3bEc2BLDezphcTEVYNGHzWzc6Vyxk3cygxcfXb6INN69h09FMmjZxMbGgMB8oOk1dVSMbGHbh7R9G/Wz8q/VXkVxVybnQXuh31U931fFwh0YDBbXy8ufZxpo4ZTf/EEVhr2VmcQW5GHvEFVQye3Pg49/zSxQwfMIiRA0YDsK/kADvTMyg5dIB50+cTER5Jua+SrYW72f7JVob37c+4wRPqfstZB48Qvb+EYTMmEhJa/7n7j6wh61AUkX2n4rMujIHygjxKM1cxZNokTIOB3VesiqYov4yxkxJwU4kFSoot2VvWMeXCITRYLGvWRXMku5Axk7rh9Tife2FxJa9/8k8unXUevWLjqfBVsr1wL669fpLCvPQZNRyXy7kfVlFZxV/e+gvjx02hf5e+lNaUs7d4P/0qvbiPxpAweCpudwgYMFhcWYupjp2EieiFtQZjILJmC56KdEoizqWaSLDgcfnoUf0cZeGp5FX2x2/dGPwcOuyiJC+X5MFd8Xg9GCw+n4u3l1YxYlQoyfW7L7bs3kxO/lbmTz+XsFAvJdVlbCnczZ51Wxg3ZDTD+o0AYG/JfnIy84nJLWt23vKft58hPimeCQPG48eyuygDm2fodriaIdNSG50npX2wge69mu5D8yjYvYqy2Atwh3fFWoPb5efQ1nX07eclukf/urwHc46ybN1rzD/3UuKj4qnyV7OlYBc7Nu8kMSaK6WPOxW3c7C89RPqRbKK3FTHqvMa/ubc/WonXU80542bhcrnIqTjKgZJMRhftoiB8HjY0EZ/fhdvlZ/Oe94mOKCB12GyMy01uZQF7CrNIe3MSY8eHkpQExlZj8ZC2pwf7D7iYMz2H8NAqjIEav5tnX0pi6qQSUnoV4XY5v8/svdl4/PmMmNADd4NjV8Xe18nxpRLVfWDdd/jAARdHD5UwaEQE3hADWHzWzWur/sv0cSMZ2acv1lp2FWeQvj+M3M19SZ0cSWSUHxcWPyG8+V43knuWMWpYKS6Xz9kflOWSm3mQLv2GExrhgcA5Tub6DxgwLI7oxPrz0NzsQrK3pTF0+sRG23PLh5vomhRN4oD6vtH5R0rI3PAJw8+Z3Gi/uG3NVmLjQ+g5ZFBdWnbuEf675imGjRxFcmwf8qoKOFyRR98DXegdF02v4UOovbdbVmp46eVqxk+MoEv3KIyxWGtYs3EF4RFVnD9hHMZlnG2Ul8kHa5cxZmwq/RNSKKkpZ1/pAYozisisSic5eSBRIeFkl+fixsWdaX+ioYXvL+SyL/Tgq1/5Mv/+j4fISIPfD+7KA/DeXCIuXAQx9fVYvWIpOTufY9L4a3F5nf3X7uIMNiwdzcBk6DsgArfx4beGnP15+EsPMnpK78bnLa9E0a1rNQMGR+IyNVjrwuXL50fXFXLLL+bxrW+58PlchPXdgj3XEuWJoGj7ekL8NRgMJUdiWfhgBF9LvYQkXsVNORZYlzGLLh+8SURs/TRseYeLOJC2iWEzJtInkHZo80qWrYgmyd7BrC534d79N2zgxpAJTYKclRzKCSHnSAh9e1dyx9d8zL9kCv98wkOfPs5+au2qQkr3rSU+ZWCjzzM9bTdx4QUMHNMnMHOrobikhLdWPMe88+bRNSYBay3bi/ZStrOUhBAXyaPr96E11TVsWPIeI8+bRlhkff+31Rs+oaziCOelno/L46agqph9JQfY9uHHzJo6h55dewGwqziDHTv2QkUxF06ai9fr5WhlAdsK95C+dgvTJ8ygX4/+db+jjIxYjmztQerUGCLDqjD48Zswdn/0MaMmJdGle/33el9mGB+uMkycEkVURDUGH34Txqtrnmbi8P6M79+vbrm5BwqJP1rJkCb70M3vradH/8RG1347MzL4aPsK5k2/mPjIOEqqy0gr3MX+TfsY1LMvE4ZNBJxz1r2Hq9j74VAmTokgvsG1wop1fXBRyYyJObgD1yt5BV7e/yCE2bOqSYirwRiw1rBvwxa6dHXTf0RXTGC75x+t5nd/DOeLt43AG+quvfTj4w3PEe4+ymXnTMTtcXO4PJfdeVmUrYlg9DkDiYyJpTbzxi1hZGWUMXlqSODaD6qrXexe9T4TLxhBZFR9u5Gq/D3s35VPzMBzcIeEYS248LH87SLGTXATnRBXt9xdu11kplcxbboncLPUOVd7avlDjBo1mDG9BlDlr2ZXcQYFB0rwlBRzwdS5hHpDKakuY1vRHrI+GESfXiH0HdQdY5zl5mQeoiIvndFTB+IJqb8xmrtjNcW+fsT2HYa1zn64IPswNbkbGDRpHC5P/fb81zvPkB9TzOCeQ7BYDlYcoV9VHxKza5qdtxTs+5TcojgSBowPLBcO5mTwzq5/MWLkaHpH9eJIZQE5Fbn03d+FPl3j6Dm0ft9TXORj+ZI8xk7rS2RsZN3nk7FpC7ExNfQf0bNuH1peWkXa8pWMnjOd0AbHxLSNJZQUlDNowiDcgQNSdXU1u95byvg54wiLrN/n703LhMoCUsaOxOWu33brX3+XYTNSiYitv4bN2JZFdekR+o8dhctTPwNjSX4RhUUhTJh/Ia1hbNNpf051AcZcBcy11n418PpGYLK19lsN8qQF8hwIvN4TyNMsaGSMuRW4FaBv374TMpo2Z7jSwIuNk1Z+dQbnuIG/fdB4WQubl/e+4pn8rLAEHl/f+I0raLbcr9/Sj0f77mux3k1dc3QGz873w4UfOgmbFkLaXc7z6xp8xpsW8vErrzFxWwY807j6xY9EEX3tcuiSWpc37c0XGDmsAi7d02i5vhdcuF9uPCrgssHdOf+65lNxlySFE3nV+9BlYl3a23dexY/+t4gNGxrn7f1zN1mhTUYbzJzKkA9XsX174+Qhffbz0N/7MHdufdru3ZCaCgUFjfOu+u01TLt4HIz4qdNBvaaEbT/pw5wHt3GwydiB82dsZvYVo/j+9xsklmbCG6Pg6sYD/37rokeJTb2Ne4B/dTQAACAASURBVO9tUumnTePP/cUk1m3tRerc6ZD6cKN0Kg47eZOS4PBh6BLJJ98bxPhzB8I5LzTKa6481GymrB2v/YUhse/COYua5WVh4xYhg4sGcfnqXzL4oll0HxSDN9xNRZmP/9saxyFv4+WGlCTyzKppXHnlQRg7DXIeA38p4z51keHys/RJ6FcAP50DT4xzUTPUX1+PmhJ4uJRuu9zkcnK3oEKffZGtc/5N/347YMgEp7/V0acoJ5nwS5dCVAq1O8UP7jqPc+56v+7EBqDKDaG/bL7c35acyw+/MBIm/rmubL6HSvHsbp73x4UzeeDKJJj+bKN6AM22EQ+X4nujP/c8dT0P8FM81BBKJbkLu2MjY2DmVyFhAvirsXv+wWN3TuKRgz9n/jWRdO0K2/bn8u/uvQmthuL7nGDGpK9BYWwfdkbtb164gH+/CAPy4cIboVck7Fxv4EXLSmbwbR7m967b+eX3VuPK87P83+Dxw0OT4cExcdz82HdYyF1wKTAN7CaY3T2EspJq3v03RFbD38fD/efA3kk0254zf/UeDw1Zztg37oKvAV2BvwAPNcgLsPQ2kl66nyO2io1/hZE5cO858IvZTj1nZMKKJ8BnIOrnUPnmo7wTncnsgffBDJwJW0pg0bOXc9U3tsGEb0DMUKgp5cOnn2BG1f+wCxr/vlr6HT1w/jf5uOYBFjX4aRQUwMg+OSz9qDvDG3Rk3vDUfYwdsAem/KP5cr/oc054A5/F+u8OYcKWdHi28SAXLe3zxxYN5tMpOXB1fqPP0lxvyc2FLl3q8x755EW6Hf0jXLCiURlqio/g+XwaxA6rS658ujuh8b1h3ieN1nfk0W50u+Il6D7j2J9PoAypu6JYT8vdEJoa/cLr3PK1i/nOdxqnm7uaN9p9sORcvnvtOJjwx8Z13tV8uZMre/NR6IGTKsPX8mfw2KXhMHtps8+y6X5x9fOLmRp9H8xb3yhvTbUbz1W7ITKlLu/G385gzKBM+Hxm89/9ZRkQ2bcu7+sPDWN+QZODEXC0ZxwJVy6DhPH1x5hfeRie08Jg8QstO3bA4MENyrsabroJdu5snHV4cjoPPJLC5z5Xn/bRR/ClL8GOHY3z7nlkPAM+9zNIvro+cXEiVOY0Ow8498U/sdKc3CAt9xXP5GdfGAqTHj3h9vx+wbn8/oqucM7iE+5D7571fQ4k3M1jjzVZSMPjZ2AZD9zenZ9ycudDEwuHsXZ6LlyZc8Lydtt+BcOzF/Pee43TW/pez/KEsnz8MLhwDTQIROY/HEP81YuhxwX1mRclQHhPuCSt0TI+uS+V8dd8HQZ8pa5sm37Yn4sf2cSBJj+DTx+cx7iLL4Yht9el2cWJuNKan2f9vWIqX/3iRTB6QX3i4u5QlQ9fbDzL5Vdn/4suE2/mgaYDNbRwfvv+Becws2c2/LvJAfMpmm3PVd8axbSpPrhobfNtf20VuELq0v/6nUS+4d/brB52EPD5LIjoWZc3484EkkcMhDnvOpkC+7MWt2d5AkfCm3+ve1UaskIb7ySMD6wbvr0aHlgGu+PhnFvgw2Qoef8n/P6FCRT3GwWeELoVfcRf7v0JkVevgKh+defCqbuiWU8xLzwPV22FuTdA9nDYODwZLktvtL4PFs5kxpXnwaiFjT+fsO5wxeFGeXkuEt4ZTubjh/kaj3GAPlwV+zue//q/mLUZ/vq6k+3qq+HDzB/zcEk4V22/y7l66Qu8AtzefBut/MY4zpnphTnLGpVh4fULnHODMcA1QBmwCraHT+Om1/9Abq8x9E6sZld6CAdnhbPyxRk8zlfYFDkN46thSsJyXuGbZGU1+eCfNnBFDoQFxj/ctJBFSx/l6tIm9QWKPw4n6vXGs3oURMQQ9/eiZscu7w6oPsm+KgMIYw8VJ84IPFp8Pv9342QYc099YtPjJ8DS23j72Q+44KoxMP73jfKazc3rdnfRTH559QDn/KLhtl9Ms9/c/305hceS00+qvN8smcAjl/ZrfK1Qu8//Yg2Y+gv2tN9NZGRqIsx6rXG9oH4bBcqW9qMULno4rdn2bGm/mFyWRHpqWf31UeC3UVHuJeyKjyB+bF3e8qf7Er54f7M6x4QWsvKjGMaMaZBYuB3eOQeuPNKovAfze9Dz8ieg50WN0lvaH4z0x5LmanwMjnFB4QCaXaO9/+kQZn6+hfOWFs4v3nvkXmYtuAfyGn+vWjoHPJXzlnse/wG/zPpDs2XYB4EmpxKbR49g1H3UH2MaXvdflgmRferWt+eO7gz4MBMWN7keewq4PMs5VgWWcXTNw3RZkt9sG+VeGEvXpS2cz7QQv9h05yhGFw6Eh5q0ULghGZ5Mb7YIY8x6a21q84U3yReEoNFUYKG19qLA658BWGvvb5BnSSDPamOMBzgEdLMnWHlqaqpdt25dm8r3WZNekM69K+/lrV1vkRSdxIJzFzDfWwjV+RDaHVxeyHiW8t2v8FH6HGpmvFY3I4UxcO65sCN/E39b/zcOFB3g8qGXc0m/q1m9IpLaDoW1jwcPQmIieL0EIubOX3Ky03S2uro+v8sFY19eiPueu+rKWkQ0D855jf43nUtEhLMcvx+Ki2HECCdm4fM5y3TZCkZ6/4I7srszrWmgHltXp7Hm8I3ETvtZ3Y/fWvjcBTmEmTxncEIsLJsDVUchMhnG/sbJmPkC7F8EL3pgcZMpMqYAd82D6Y+CJxK2PgDbfsvW7AnkTFiH30+gBRSE1+xiUs9XcEf3BlyQuQj2v+BclJYe5e09b1NQWcDcgXNJiUs5DVv92Hx+Hze/cjP/3fRfAOJC43ju6ueY3W82y/ctZ/HWxSRFJXHDmBvqZu5qpHArrLgC5rwDEYE7MYEdY0s73I2HNjLjiRl1g0X2i+vHulvXkRCe0GzRmw5tIvXvqVQHpo2P9kaT9o00+sYGLhBrv2jWwsKFcNddzZZx96xlPLB2NrNnw7hx8M7ruay+tBv2J4fAEw0e587Yf27/Jk/v+D2LXgwjJMSZTrewuIaufwzHWkv2D7LpFtmNhAcSKKos4ivjvsJjn9RfSc1KnsXXJ36daxZd02j95/c7n7e/1LgFVbtq8Jn1f6g/+4v28/p1r3PhgAu55ZVbeGLDExgMPaJ7kPX9LA4WHyTljykAVM66DHPwTaf7W1R/2PBT52J9/rbAb87T6OTA3r7e+R3u+ivs+ovTNP+aEwdAioud8SLuu88Z7LT2d0TZAaLLPwRPoJ9f+jOQ+Sy8GAKLq5svaMEC53vRxP7C/by47UUKKwq5fNjljOg2Atfddzf7/qz5yt85NP+ruFz1H5vHn8ec4UvwhgVadWY8C5nPgSsMpj8VmFLZQPrTTtk8sTD1304kLv1ZJw0gdjSM+VXjvMepR/kdP+Fo2VF6RPfA7XI3zwN88AGkpUFCg5+SzweXXupnU95HPJv2LNGh0dww+gZnrI0WfjOVC+7gr3O78vj6xwlxh3D75Nu5eczNvLLjFb6/5PtkFWcxuMtg/nDhHzi///m8s/cdntjwBC7j4pZxt3BeynktfpYffPkfFF55C9D4Z3vR6oV47m2Q1wCPTIALvuqcyBoP7P4b7Hm85f3wHy6BSy6B0C7OOF7pT0Pmc/hNGFvnfMx7+95jaNehTOs7jYh7f9PiPoIFC6j+5R3szd9L98juxIfHYy1UVjqD3NYet3w+p8ttRUV9Vw9j4JNPnG4y0dHOeGXWOgPybtsGkyc7A/V6vYH/KdnHzPE7nbuvFtj3JGQ8Ba4IuDIbfIHjEQZcHl7f9wF3LP8F+wv3c07yOdwz+x4GJgzkwTUP8sSnTxDmCeM7k7/DLeNuwdx1V/P6LVhAzZ2/YHvudjwuD4MSBjnfn5b2lzNnwvvvN0razQCWX/d34i8/r+536PfDFZuabLeA9AXf4S/TvSzbu4xRiaP43pTvMSZpDPvy97Fo6yKKKou4cviVjO4+usXviV1wJw/Njee+lfdR5avimhHXcFOvX/Ofx+KZO9cZl8XjcWaHmjLVz50rfszv1zgXhFN6TeHZWd8jee+fnAu/sETY+mvnL2YkDP4WeGOdi7TMF2D/CxASCxOdcXLYv9g53kOz71olXt69/p/Y667H76//DnerWcL4/hvxxPRzAi6B/UGNCePl0U/y5KYnifZGc1vqbcyw+yF3NfS92sm75x/O99oVCue+Qt106vv+S+mOl8gsGk3FzNWNziVGj4YGjeWP7xjHxJa2c4tpAAsWYBcsIL8in9jQ2GN/d7zAr74KN10P1g8fXAVV+VT4DbMrp7D6wGrAOYa/ef2bTO87HZ/fx/6i/XSN6EqUN6rZqoNlRfoKZv57ZqO0nt4w0i+6nZD40RAS7Ryn9j4Jmc+AOwKuKa3fyJX5sPFO6DrJCR65vLDnCUj/T33etmo4GscpXHPl5MDQofCb38D11zvn1SEhwRnw+i8f/4VvvvHNutezU2az5MYleBr2BKjVwneiZMHPGBT1RKOZXh+Z9whR3ihufuXmurRe0b3Y/q3tZBdn88SnT7CvYB+fG/w5Lh92OQUVBfzw7R/y4tYX8bq9/HLmL/mBezcmrAuMCdwJrr0Qb+n4eQ3ww28FgkYu2HwXbLkHvwln06xVPL/1eSI8EVwz8hoGdRnU8nf7GOcRZVVlLNu3jFX7VzGtzzTm9JuD1+3lzx//mT+v/TN+/NyWehvf7R6P5+BrMOkx53wz7V7Yej8YL0z6q/MdClyvsP8F5zzpgpXOYOnLL4KqvBYDV8cr21u73+Khjx4ivzyf60ddzy3jbqkbP7OR7LehPDswBqaBzOedfWAL3+viYieu2fCY6DKWockH8VDi9HZ4Z5ZzHQXOOdHctU7GZbMD6S4qvlDK5sObiQuLY0DCAAyGx9Y/xg+W/oCy6jJGJ47mkYsfYUa3QVBVALba2afULqOFz6LkZ/dS8I2fU1VVv690u6Hv5CTM4SYBwsREOHSI2vBC3Ug4LWz78p//isLbf0FVFXX7fZcLevUCjJ+jZUeJD493fhO1gd+mErvDwSzn86k9tv/q186AbE0dY3ueDc5k0MgD7ATmAFnAx8B11totDfJ8Exhlrb3NGHMtcIW19gsnWraCRtLu/D7wlTk7BJfXCSCd5HBcKSmde9yf5GRLenrLn8WO3B1EeaPoFdPrhMt5c9ebRHojOTf5XCfhJA/sOTnOZ/y73zl3/cPDoeroLiL+Ohj7y8BdVYBNC5l742Quu20eX/9648X2ebAPB4oOsP7W9YxOHI33V05zr5Kfl+AyLv614V9cOvhSesY4dwG+89Z3ePgjp6Va7+je7Pr2LsI8Z9Gg1A2u2Kf9YxprDqzh6Suf5tqR13LJ05fwxq43MBi8bi8Vv6jg46yPmfz4ZC5O6MZriWVw3lKIH+2cZDzjgYi+MH+7cyHm9jYOGi1o27HDWigqgvJy50AdFaXZX0TkJFi/c8Hhr3aC2d6Ekz4uS/CVVJVQUlVSNy7fmTbzXzNZkbGCUHcolb5Knrz8SW4YfcPx/6lhdPt0a2XQ6I47nJuzf/qTc3wMNr/1s2zvMlJ7phIfHt+qZbyx8w2W7l3KfXPuq5vwo9pXzR9W/4Hpfaczo++MEyyhBdYPZVnOMAnWB+5wiB7Y8m/cWqjMhepCJ68r1Lk5fKb3BzXlUF1Qf60Qlqh9knQYJxs0avOYRoExir4FLAHcwD+ttVuMMXcD66y1/wP+ATxpjNkN5AHXtnW9ImeEyw2u6Fb9a3p6cIty9jn2AXFI1yEnvZR5g+Y1Tli48KSi8UuWOHdmb7qpfsDM8O6B/sYrr4Sxv3ZazAz/ETf+Xzr/ehFuvNG5QxEeDiUlMKTLEA4UHSCzMJOuEV2xWLqEd6k7+bkt9bZG6/zjRX/k9Z2vsyd/D5u/sfnsChg10SemD6tZTV6501Ugt8zpDut2uan0VVJcWUxWcRYWy/VxodBzNsSNcgKjtcoy4b350ONCiB0K4/8AB9+CXU+2uXzGOK00YmPbvCgR+SwxLght3npV2keUN+q0tiY6kSU3LCH83nAqfZV8f8r3jx8watpiwJi61glnm7Vr4YYbTk/ACJwJOC4YcMGJMx7HxYMv5uLBFzdKC3GH8JMZP2n9Qo3L6dpzUnmN062rtvtde/GE17eSFumkgjEQNtbaN4A3mqTd2eB5BXB10/8TEWmtV1+FCy6g0YCWdSY+Cgdedro4ubxcN7cflaHDmDrVxUUXOVP3ZmTA+CvGs2zfsrqgEcCghEEtLNBhjGH3t1sYjKm9tXAinHI+MKM+WFQbPOoW0Y3skmyyirPIKnI6zM8KKYHkL0BI4Oz0xQZ3jM9f5ozLUZXvNEnucxW83fagkYiISFuFecJ49Yuv8oMlP+D+8+8/8T90EGPHOjPrXnVV45nkRETaQ1CCRiIiZ1pGhtPPv+FsS3UiesLgb9S9NMAt/eGmm53YSlGRMzbMEzudQNEd796B1+10TYsLj2thgR1Pr2LnsXaK4MIKZwC9lLgUskuyeXn7y6w/6EwIEGN8ENOgdVhFk77b3njnT0RE5Cwzf/B85g+ef+KMZ2GLomP53vec8T3POw8uvdQZ0yg0lMCMVSIiZ5aCRiLSIblczoCpp8Lthp49nT+ArgecqbNqB+4GZ7DwDqeFE+EeW16ARV/gcIkTACqqLAKoGyDw7vfvrhuE3I+BmrL6fw5LDASOTnJ6FBEREQmanj3hvffguuvg/vuhd2/Yvh327GnvkonIZ5GCRiLSIY0c6ZxQzZvX+gGUz+9/frO01J4nHAuuQ6gdlPRI2RFq/DVU+ioJ94QzuMtg1mStobymfmrdqpjhkP0WxI12Bry+ouPcjRURETnrtDTj0imOoTRmDGzZAnv3Ov/Sv/9pKKeIyEnQbWQR6ZDmz4d33mmc5vef2jKS45LrnocEZlubO3BuW4t2VkiMTAScmezuWXEP4AxYOq7HuGZ5Ywbe7ExnbhtMaduw5ZGIiIi0i/79Ydo0Jw4lItIeFDQSkQ7p/PMhOxtuvx3Kypxxio4ePfXljOo+CoDYUGcar+HdhgezmO0mOtSZ9S+nLId7V9wLQGl1aV1QLD7MGaPI6/ISknIdVBxyZp2rPAp+H+xf1D4FFxER6egOHXKmhG/614HGVRIRqaWgkYh0SJGR8Pe/w7PPwvDh8IUvwKBjT3x2TGOTxgKQW55LYmQixpggl7R99IjuUfe8xjqDP0V7oxncZTAAkV5nOpbUXqnOrGnTX4CclfBiIjwfDmu/0XyhIiIiIiLymaKgkYh0WFdfDS+95HRLe/ddp///qRqXVN9da0rvKUEsXfszNA6AJUYl4jIuwj3hHCg6AMDIbiOdN3vMgfnbof+XIekimPy4c1dUREREREQ+szQQtoh0aBdeCJmZ9a/NXaf2/0O61k81P77H+CCV6uzgdXup9FXWve4T0wdw6vnh/g8BGNF9RP0/RPaByX8/o2UUEREREZGzl1oaichn2tCuQ+ued5bxjGolRjmDYXtdXqC+VVXD4NjAhIFnvmAiIiIiItIhKGgkIp9pybH1M6gN6TLkODk7ngsHXAjADaNvAGBWyiwAhnUdVpdHQSMRERERETkWBY1E5DPN7XLXPR/UpRUjaZ/FUmJTAMgqzgIgKcqZr7dhoCglLuVMF0tERERERDoIBY1ERALCPGHtXYSgqg0S5ZTmNHrdMGjkdXvPfMFERERERKRDUNBIRD7zasf86Wx6RPcA4EjpEQASwhMA6BPbp93KJCIiIiIiHYdmTxORz7yXr32Z9IL09i5G0NW2LDpS5gSNjDEAeFyeRo8iIiIiIiIt0RWDiHzmzRs0r72LcFrUBo0qfZVEeaOavR8ZEnmmiyQiIiIiIh1Im4JGxpjfAp8DqoA9wJettQUt5EsHigEfUGOtTW3LekVE5MS6R3ave950lrS8H+dpPCMRERERETmuto5p9DYw0lo7GtgJ/Ow4ec+z1o5VwEhE5Mxo2P1seNfhjd6LD48n0quWRiIiIiIicmxtChpZa5daa2sCL9cAvdteJBERCbb+8f3buwgiIiIiItLBBHP2tFuAN4/xngWWGmPWG2NuDeI6RUTkOOLD4gHoGd2znUsiIiIiIiIdzQnHNDLGvAMktfDWHdbaVwJ57gBqgKeOsZgZ1tosY0x34G1jzHZr7YpjrO9W4FaAvn37nkQVRETkWJKiksivyKdHdI/2LoqIiIiIiHQwJwwaWWvPP977xpibgfnAHGutPcYysgKPOcaYl4BJQItBI2vtY8BjAKmpqS0uT0RETk6PqB5sy91WN5OaiIiIiIjIyWpT9zRjzFzgx8Cl1tqyY+SJNMZE1z4HLgTS2rJeERE5ObUzqCloJCIiIiIip6qtYxo9AkTjdDnbYIx5FMAY09MY80YgTyLwgTFmI7AWeN1a+1Yb1ysiIiehS0QXQEEjERERERE5dSfsnnY81tqBx0g/CFwceL4XGNOW9YiISOtEeiMBCPOEtXNJRERERESko2lT0EhE5GyTHJuMucu0dzHOOsH8TJJjk4O2LBEREREROXspaCQinUr6d9PbuwgiIiIiIiKdQlvHNBIRERERERERkU5IQSMREREREREREWlGQSMREREREREREWlGQSMREREREREREWlGQSMREREREREREWlGQSMREREREREREWlGQSMREREREREREWlGQSMREREREREREWlGQSMREREREREREWlGQSMREREREREREWnGWGvbuwzHZIw5AmQ0Se4K5LZDcc6Ezlw36Nz1U906ps5cN+jc9VPdOqbOXDfo3PVT3Tqmzlw36Nz1U906ps5cN+jc9fss1i3ZWtvtRP98VgeNWmKMWWetTW3vcpwOnblu0Lnrp7p1TJ25btC566e6dUyduW7QueununVMnblu0Lnrp7p1TJ25btC566e6HZu6p4mIiIiIiIiISDMKGomIiIiIiIiISDMdMWj0WHsX4DTqzHWDzl0/1a1j6sx1g85dP9WtY+rMdYPOXT/VrWPqzHWDzl0/1a1j6sx1g85dP9XtGDrcmEYiIiIiIiIiInL6dcSWRiIiIiIiIiIicpp1qKCRMWauMWaHMWa3Mean7V2etjDG/NMYk2OMSWuQlmCMedsYsyvwGN+eZWwtY0wfY8xyY8xWY8wWY8x3Aukdvn7GmDBjzFpjzMZA3e4KpPczxnwU+G4+Z4zxtndZW8sY4zbGfGqMeS3wujPVLd0Ys9kYs8EYsy6Q1uG/lwDGmDhjzCJjzHZjzDZjzNTOUDdjzJDA9qr9KzLGfLcz1A3AGPO9wL4kzRjzTGAf05l+c98J1G2LMea7gbQOue1O5bhtHA8HtuEmY8z49iv5iR2jblcHtpvfGJPaJP/PAnXbYYy56MyX+NQco36/DewvNxljXjLGxDV4r8PU7xh1+1WgXhuMMUuNMT0D6R3+e9ngvR8YY6wxpmvgdYevmzFmoTEmq8Hx7uIG73WY7yQce9sZY24P/O62GGN+0yC9w9TvGNvuuQbbLd0Ys6HBex29bmONMWsCdVtnjJkUSO8Mv7kxxpjVxrk2eNUYE9PgvY603U7p2rtV285a2yH+ADewB+gPeIGNwPD2Llcb6nMuMB5Ia5D2G+Cngec/BR5o73K2sm49gPGB59HATmB4Z6gfYICowPMQ4CNgCvA8cG0g/VHg6+1d1jbU8fvA08BrgdedqW7pQNcmaR3+exko+7+Brwaee4G4zlK3BnV0A4eA5M5QN6AXsA8ID7x+Hri5s/zmgJFAGhABeIB3gIEdddudynEbuBh4M3DMmAJ81N7lb0XdhgFDgPeA1Abpw3HOwUKBfjjnZu72rkMr6nch4Ak8f6DBtutQ9TtG3WIaPP828GjgeYf/XgbS+wBLgIzaY3pnqBuwEPhhC3k71HfyOPU7L3AcCA287t4R63es72WD938P3NlZ6gYsBeYFnl8MvNfgeUf/zX0MzAw8vwX4VQfdbqd07d2abdeRWhpNAnZba/daa6uAZ4HL2rlMrWatXQHkNUm+DOfCj8Dj589ooYLEWpttrf0k8LwY2IZzcdTh62cdJYGXIYE/C8wGFgXSO2TdAIwxvYFLgMcDrw2dpG7H0eG/l8aYWJyD4T8ArLVV1toCOkHdmpgD7LHWZtB56uYBwo0xHpzgSjad5zc3DOdEpMxaWwO8D1zx/+3dW4xdUxzH8e9filCCutSlpDQtT1IaQtQldQmJdOIa0gQhEbeEPpBQkXiQSNTtQUhcg7orxgt1afCimGq1tKgQnVanJS4JSd3+Hv7/03MyMwennTqzlt8nOZl99t6T7F/22mevvfZae1PovuvwvN0DPJrnjHeBXc1sn/9mSzs3XDZ3X+Hunw6zeg/wlLtvdPcvgVVEHW3UapNvQZZLgHeBCTldVL422X5q+TqWqKdABeUy3QlcRzMX1JNtOEWVSWib73LgVnffmOusz/lF5fu7fZf15nOBJ3NWDdkcaPTA2QVYm9M1HHNTgLdz+jXgrJwubb91eu3d8b4rqdFoP2B1y/f+nFeT8e7+TU6vA8Z3c2NGgplNBA4jeuRUkc9i+NYSYD3xA/MF8ENL5bPksnkXURH7M7/vTj3ZIE58C8ysz8wuzXk1lMsDgQ3AwxZDCx8ws7HUka3VeTQrYsVnc/c1wFzga6Kx6Eegj3qOueXAsWa2u5ntSNzZ2p8K9l2LdllqrrPUmO1i4q4rVJLPzG4xs9XALOCmnF18NjPrAda4+9JBi4rPlq7K4SIPWXPobi3ZphDnhEVm9paZHZHza8kHcCww4O6f5/casl0D3Ja/J3OB63N+Ddk+ptkJ5RyijgIFZ/uX194d5yup0eh/xaPvWNGvtjOznYDngWsG3fUqOp+7/+HuU4m7kkcCh3R5k0aEmZ0OrHf3vm5vy1Y03d0PB04DrjSz41oXFlwuxxBdbu9198OAn4luqJsUnA0Ai+f6zASeHbys1Gx5QdBDNPrtS/QIOLWrGzWCz9CekQAABDBJREFU3H0FMexnAfAKsAT4Y9A6Re674dSU5f/EzOYAvwPzur0tI8nd57j7/kSuq7q9PSMhG59voNkIVpt7gUnAVOJGwu3d3ZwRNwYYRwyHuRZ4Jnvm1OR8mje3anE5MDt/T2aTvdorcTFwhZn1EcO6fu3y9myRrXntXVKj0RqarX8QF+xrurQtW8tAo2tY/l3/D+uPWma2LVFo57n7/JxdTT6AHP6zEDia6NY3JheVWjaPAWaa2VfE8M8ZwN3UkQ3Y1LOj0SX6BaLRr4Zy2Q/0u/ui/P4c0YhUQ7aG04DF7j6Q32vIdhLwpbtvcPffgPnEcVjTMfegu09z9+OA74lx9jXsu4Z2WWqus1STzcwuAk4HZmWFGirKl+bRHHJRerZJRCP70qyrTAAWm9nelJ8Ndx/IG5N/AvfTHA5TfLbUD8zPITHvEb3a96CSfHnePhN4umV2DdkuJOonEDfuqimX7r7S3U9x92lEY98Xuai4bB1ee3ecr6RGo/eByRZvldmOGKbQ2+VtGmm9xIFJ/n2pi9uy2fKuwYPACne/o2VR8fnMbE/LN6yY2Q7AycS40YXA2blakdnc/Xp3n+DuE4nj6013n0UF2QDMbKyZ7dyYJh6CupwKyqW7rwNWm9nBOetE4BMqyNZi8N27GrJ9DRxlZjvm72Zjv1VxzAGY2V759wCiMv0Edey7hnZZeoEL8g0lRwE/tnQRL10vcJ6ZbW9mBwKTgfe6vE0dM7NTieHYM939l5ZFxeczs8ktX3uAlTlddLl092Xuvpe7T8y6Sj/x8Nd1FJ4NNl3UNZxB1FGggjKZXiQeho2ZTSFe2vEt9eQ7CVjp7v0t82rIthY4PqdnAI2hdzUcc406yjbAjcTLR6Cw/bYZ196d7zsfBU/8/rcf4nkInxGtgHO6vT1bmOVJouvpb8RJ7xLi+TFvEAfj68C4bm/nZmabTnR/+4gYjrAk913x+YBDgQ8z23Kab0c4iPgxWUW0wm/f7W3dwpwn0Hx7WhXZMsfS/Hzc+A2poVxmjqnAB1k2XwR2qyjbWOA7YJeWebVku5m4oFsOPEa8qaOKYy7zvUM0hC0FTix533Vy3ibeSHJP1leW0fL2sdH4aZPtjJzeCAwAr7asPyezfUq+VWc0f9rkW0U806FRT7mvxHxtsj2fvykfAS8D+9VSLgct/4rm29OKz5bngGW533qBfUosk3+Tbzvg8Sybi4EZJeZrVy6BR4DLhlm/6GzEdV0fcR5fBEzLdWs45q4m2hY+A24FrND91tG19+bsO8t/FBERERERERER2aSk4WkiIiIiIiIiIvIfUaORiIiIiIiIiIgMoUYjEREREREREREZQo1GIiIiIiIiIiIyhBqNRERERERERERkCDUaiYiIiIiIiIjIEGo0EhERERERERGRIdRoJCIiIiIiIiIiQ/wFhVtciPyUwJQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "guided_backprop\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "integrated_gradients10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rescale_all_layers\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "revealcancel_all_layers\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vg/0/Qfr+Ey/7b+F2ec/EVZ0kLDHl6zQBzvoM+kyBdvdVdalKlrwJancx3XtPJGULhDSvvt3nKosz1cxe1uHR/MBYde/en7DElDm0BfT5ElrdrNfsGkD7EShV3vzrmMwePE+8TzSgb1nZAkzDVfksIwPq1/ehCxeYzB7JMSnfgfVPboOuVAhqdHL/WxZpu82sF8GN/t2zXRTmdsHcPpC6CyQbMuPhlzNg1OGqLln1tW+myUw7bx6EtQVxmUBosct+b1L6uz6TX6Hv+d/KK6uqGdL2QOIaaDwc+kwF3LDhxaou1ann0AJYeEn+5BGjDusU42Du69W1O3XcD+BXy3Ttyb2Gdn22ast0PGk7ocXVvk0WkrYHarUDWzXON9jwMqydYLop9Z0KDS8s/22k7jA9AaIGlP+6K8qOT02b44Ilpj7ceLipC6hqrZpe5WqoxDXwz9PmROj6rOkLrP59/GqbFEdbgQF9SzOYb2n51zKNVeUzpxMcDh/rGo5Qk6WQssM8uToZkuM9Xk9lOPw3zB9sBoW27ND4ksrdfnW2e5p5OlW7E7S60QQyEhZXdamqt7gfoH5/CGnmmRHGHxqVcEwdjYVmowpluGlXU1VI3BzzJPusz8HhadB2eKRqy3SqSdsNC4Z6P32vqkBP0kY4NN9Md97gvKopQ0GWzfu6VJ0mtzi6Appd4X0NdedU355PmfEQ2sq3ZXPSzH2kuvrnGTNekjvTfC2+DEYl5s+kWl5St5vPzO2sOVlOB+ZC6xtNkMqymet3d+3GVd1V47BqDbP/F/OkeO8M2P01/NTNnMDq38e/DmQllDi19uTVk+n9v97luL16kHkg//fAYsaMUV5ESjHOoc3PBEuS/jn5Bqu4fXviVV6cqTB/CDiTIKSxCTzu+75it5m0Eea0h68DYcHFkJ1Usdsri02vQmhLOH8hnHab6WoUdW5Vl6p6S9oALa4rlM1TwkmUsgUaDfMOcP5bB+BUJTu6AqIvwKvLc1kGwldFrXvaZDL2eAsuOwzdXqqacuz+Bn7uCasehIWXwvI7vQMZVcFyeM9+VJ0mt0jdZrJJvK6hPnSRqipuJwTU833Z6hrcyEww4805gqHX+9DlWZO5Ut6BHjCZPcGNK3F8zzJyO8050uo/OtlIDaN7qDy40uGPsYCYMQpc6bD9fzoY67+Vf20zBoErxQR+Cnlx6YtsPLwREcEqj5kVAutDxgETiLDsFZ9JVNEyDppMC3sINBtdITdZf3+T3eN2+5jd4xduGrCuNPNzTimnx7Qs74ptRT9B3P6xOR76ToVGw02Fdv4gYFn5bwvMNJy/9DafDwIHf6u+U4i6MuDYOhj4I9g9FRZHMPR8p2rLVZ2JGzL2Qd2e3gOVO4qpsLtzzLEdflrllU/VTIlroO2d5pqay67di8qN22mmR+7wMLQaZ57Cn3Z75ZcjeQv8db25dtQ/x1x/d3wGvaq4a6fNr3RZ0Q0awKFCXfKjosw4feXJnWPqQWE16BoqOb4HakW87yPVKaNq+8cmEDUkFgIbmEBP3dMrZlsZB02wp7qOG1VY6k7zsCekuffr2iW02tNgT3nY+YW5eA1e5kljdEOT4VVdKlVBkpNh715o2hTCintY7V/HVLLSdhcb7Nl1bBcAe5P30rRW07IXKKgBpO81Qcaa/vR8/y+w5AoQp6kM/PMUXLy53J8cBASY/ZidXfy4PdPXT2f6hul8c/k35oXA+mbWmNzKTO5UqL6ybOY95aroJ4jb3odmY6DhJZ4GeRAM+B7+alAx21t+O1h+ZhuRfWHLuxAYWTHbKqvkjRBQHyLP9o70BURUXZmqu4wDZv8GNznxstlHTCMqsIKONXXqSN0OtbtVdSlOXUkbzZgjHR6q2gFg1z5psj7OXwwBdQGbGUOoqgVEmAasuH2bbbNwoKek18oqO9E8uAuKLv91VxgLrwy94wVwLLv3DJjVKaNq3yxofrU5XnO7K0f2q5ht5WSYQHdNCZakbDFt3JxM72QGuy+DX6qqpN24ysOeaaZhFdLcNKwcIWZWCXVKEYH33oOGDeGss6BRI0gvbhB6v1omq+HoymLXk+EygYIV+1eUT8ECoyF586kxJsaSURBQB05/Azo8Zp66VUCKaFQUHDgAWVnF//3en+9l+obpSG42TlBjk3LrSjO/lzbbyOZv0oMrg9tpGlEdHvZO+y6hW2GZpe6CI39C/+lmmm3/OtDu3orZVnk4ttYMDllwiligWlQ0qytXmicY5kO3C3e2J0W/BmcXqorhTIVV42FuX1h2q8l+rc5jd9R0x9aYceaqsruUOwf2fQenvwrBDU3j1i8U+n1ddWXKFdrcjN2Wey842S7wzlRYcTdMr22CHFs/KFvXHHe2pwFdg5poNju4C1SojhfAsTk8U3dXMyKmu3KrG7yDohXV5Uxc5nPLzfD/toHJyp9aTQdmcqaYyT5OhbbGv0wNupJUY0dXeQasKtCwqukZFqqIyZPhwQehXz+YMAF694bg4sbctTlMquORv8GZ5vWnI+n5s2b9te+v8ilYQAQkb6v8AYArQlAjuGitSTlvdx8MWV4hm2nUCI4cKbkOvD/VTE+7PdEzHXfuYOu7vzaVuP0/l26DAZEm06syJG0Av7pFG1ElzZxUVglLoVYXiDgz/wlPdZ4+NH2fCcwXHkPJXo3HQ6hqbqfJavOlkieCqVoUespbnSuxquJlHITZbWDre2ZMu11TzbW0pjzVromOroawNpU7XlxhyZtMfbjRpd772lYNxmwJaQYZh/LH1xx5gu5YUcUEgxrWh7lnmlmKIvuYseBW3u+ZkfVkCUWC5dX9GmoPgvQ435b1r2senlU3mZ5jIbyd9+u2CvrM7YFmSIDc2ayqU4ZTcXLrAarG0WBPWWUmmKcCtTpUdUlOHSJwcB6sfgzWvwDp+6u6RGRlwd13w0MPwYwZJujz3XfH+YfgxrD/xyI3idgDsXk//7DlB/NDgwYmsl/4q4GP3SCCokCyis8cObYeFlwC/xdhBs/d9ZVv66wq3Z4146jYHCZLLqhxhWymUSPTzd6/mHZGliv/6dSi3YvMD2GtzU1u7RPwzyRYfmvpNhjcBFK25v9ekYNop+2CWu1Nt76CKqrCEr8QInpRrTM53E6IX2ymIHammMB84UpLdZ4GtqrZPIGewuMsFNf4sPmDO8O7gVndK7Gq4v15rbnuDV0PF66CS3dirhkFjonq3qCtabIOQ0jTqh3Y92gs1O5aNJPScZxM07S98PdNMLst/HaOGQOuIgRGQ9p234NhBw8WmmFQYO59ZjKCIbHQ92sYONfMLudf6+TLZfM3WTIFb6nV/Roa3Nh08/FFSHMTBMztylVdJhXJOmIeOPqSwVoe/MJNF+mcElLMqxubo+g063rNrhG0dltWziTPCPT6UZYLVwb8PhCWXmFm6tj7LcxuVbThWsmWLoXoaHj4YQjxJC2EHC95Iew0k0GQtN6rcrAszgyQGx4QzobDG0w3oZL6fPvaFzysrdnGnv8retP45QxAoPtL0HAoxFbT7jW5gaoGg727bVVQJbVRIzPukstV9G8Fu9f9uPVH80NoSxOEcibDuqcKVLx8FNrSdB/Kfdp3oieIZZGT5cmsqaQKS8pWqHdG9c0s2zsLvo2GxZfD0ith0xueFHvP55NXWdEKS4kcISYbo2Cwp6TGh1+4adhlH6204qnKMXr6aIKfPYnzPOMgJCyBAd+ZbgCOEFNvsjm8Z+2rjg3a9DjYNxviF5kuSTVKoUFzq6Jhln0EQpoUnf6ypIyuxDXwQwfP7H/XmOybxZebrlLlLaiB6Ybsy3g9Jdn2IXR73pQzt4ta9OCylcsv3HTlyjpctvVUptDWZjiB3Dro8QI44W1NZk+Op15fkfWh0hAn2P28u+BV5DkT0sJ0IywpYzZ9n+kSuPU9SNlW/tsvLXuwGVrBl3qAqlY0H6siVKeR5Wua2LvNTe7iLeZpi80OCX9WeUNy7lzo3x9yfN2dtbtA3GzzNLPXe3kvz9o0C4CGYQ1Jzkpm4+GNdIiKKj6wU1zKcHHC2pib5qbXoM0t3k/QOow33aH8Qs24G21u8fENVLIMT/pvWSpdpdCoEWRmmiDehRd6/23BrgV5P8/YOMP8ENqqbIkrYW1g+yeVk05v88tPS8+Vd02qAG6nd7et6nT9S/gD/roOTn8dml9lGhx/3wQZ8Z7ZwoIKfS6VX2F54vcnaB/Znqu6XFXp2/ZZUEOzn9P353dpDIwq/phyBJlxy1J35A96XdKyqkb5ZoMZsD4+LZ76IfV9/8cDv0DEWWY8r4LX+MAG5gl/iA8Df1c2V7q5f8cvhTqdTVAqdTuMiKuYaZgrgiPEBF1zss1gs1XRMBM35mFogXFJ8spR6B4hAosvg9a3QJenTNcgV7qZ5bYiHvwENzWZCglLIHpQ6f8/45B5L01GeB8TJyjrsWOwYgU4HHDGGRBUuEebPcB0dUrZnh80qe7X0PB2cPBXU/+0Bxx/VtjQFmYcp+rWJcgWYIKKVoHBhwueMxkHYM8MU18NO83sd//aJa4uO9tMBFKrFvgVNzlzWCszQHhu9/eC+3jFPWbGuuhBJii+5kkYtu242/OJCBz+C+K+N+dW/f7Q6GLfBlkOa22ugQXHMKrux6UCNB3FcKbC+hfhx24wqyn8fi7s+tq3Qe38apnUv4I3T410nhxXGuz8EvpMMTM2+Ncy44xEVv1g13Fx0KpVCbNvFadOF1PRStoAvw3Iezm3G9e2oyZK/8GKD4pPDRbxfTpPR5BpVKXvMX3Fd3xuXveva2bhyK14OEIgsKGPb6CS5T7ZqKQnKuHhZrylzz+HFE+yTe73L9d9CUCzWmbMm21HtpnMHFcpZ+AqKKwVZB402REVLbC+ybYpeEOuyJux3RMwyd131en6t/ZJ6PAINBttKqA2f2g0DFK35gfeqjCFPN2ZzrNLnuXqmVf7tPyBlAMcTquCp72WzWRkJBYYdP54T2NDW8Khhb6PhVGdpe2Gja/DX+Ng1cOmK2BVDnhbRfYk7cn7+et/Sjm4bso209XdKtTiCW9nxrbLPU6qS3cOgD+uMl1phm2Bft/A+fOg71c1J9ADEN7eBNNyKiYzWkTwf9qfx+c9XvJCjlCToZJ7jz9ecP3YWtMA7fqMqa9YNlN/CW1V6rJlZMD8+TBnDmzdWsJC4W3NPl7/fP7kC6XayL6i01AfR2YmXHst9OoFX38Nn30Gbdrk1z28hLYyXaRzss3v1f0aGtocjq3z7YFWSPPKqw+VRnBjU66cEo6Fn043ZQ5pYbL23cV3v9q7Fy69FNq2hUsuMfv42LFiFgxtCZkHTBAJvPdx/EIT3DnjI+j1PgzbXvZAjysD5g+GVQ+a91q3O8TN8X2w7LDWJnhc8Byu7sfliWQdheSt1XPA8HKkwZ6so/BDR3ClwvkLTT/yMz+DhoOLpp0WJzDS3PyTNhZ4rUCFZcu7sOBi+P18WDgMdkwu+tRdGUdWmAphYKEnhrlZA2m7Yd/3JhB3YO7J3ZxPUkAApKaCs4RdN3LaSByTHLhzG7y1O1O4IrO2QMKNy9NXeeHuheVTwLo9zPet78FyT/ZOZL+i3br8qunAuf71zHdbCU9UypllmQG2p02DnTvNE5idOyHblc2mw5uA/MbNW8veMjfZwtllnSb6vsGIPua8X3G3qcwWmQmqjJI2wd6Z5tzISjTpvwVvXoUbUSJlmy2koHq9TPZd7vlYXRps7hxTYWp9k3fmUb2eJhgmBQIRV1Zs433xYpMZOGAAnHcedO8OL78Mn8Z+kbfM2kNrj7sOZ46Thq81JPKVyPxZ4ipTeDvY+YVvlaLanWH3VyVWhmuMLe/AvEEm0NX+QWh8KWx9v+K6qBU3fpuvY7dVsC/W5B+rLyx54STWUMyA3Qfnm67aucdJwYZD2l6TDbnxVdOVIXH1yRX8ZKTuMsHKPlPN4ML+tc01JLJv5ZWhPNTpZsbtK5g5UI5+2PoDTreT5xY/V/I1qXYX0zUr9+HD8cpwdIV5uFek3uJ7Vk9OjhlbsVcvk7m7cye88AIkFBdXsDlM1m38AjPGojvb5+0AJrAhTryaUsd5SPWf/5iu46tXw2uvwVtvwfr1JTxErNPFXEOlhrQX6vYydVxWl4sAACAASURBVJxt75u6wPHqF/V6mwDkmifMw3Z3NXgwBOZcD4gwbZFcucdrULQZa6zjo9DmZpN5VsyxnJgIp58OF18MGzfCTz/Bpk1Qu7g4TVBDUxfb/lHRoSp6vmUe2PrXyv8qq3+eNsHXc3+FNrdBy+uh53+LtrlKYg80dfVdU2vOOEMlOfgb/NgFlt8GW9+BP6+rWd0mS6ma5dBVgfXPQpOR0PGR/AZBSNPSraNOd9j+sUnr8wvNT1+0+ZubQf8ZptImOZ7ZJ4rL5/uXcLvMZ1JcIE1c5jMrnN2R2+hvfo2ZpSm4CaRuMw3mss7643aakz5xTYH1PWTKUCDdvHdv+PZb87SocDpmQloCMzfNBGDy6snc0P0G03+5UP/+7VYtIIno0GjaR7Rn3q55pGaVUz/0+gPN+yhYWSnc0Mr7LKvhU+ncmaMSV5p0f8hLD5298mLe+hSaNjXjJB05Yp6QPfoo9Ox58pscOhSWLIEhQ8zTlzlz4IN5cwGwW3YCHAGkO9OZv2u++Yf650BcgVG5u8T4vrHgRqbBEPc9rJ1g0pjLQ8YhWDraNK6bjTEV6qR/IDDCVCDa3msyvwqmVP92DtTqaILU6fuh9/tle2IU2deMBZWbkn289O3KJC5TkSo4C1nB68nm/0L7B0wQL6eUlfxSmDoV3nkHpk+H0FBTrw0IAJdLaPDW/XnLTZw/kZljZpa4ng9jP8z7+adtP3FRm4sqrMzFih5iBijPjDd994/30KLhhWYMsfglEHVO+QUWK1PyVtjwMly40jQCcu/bdbpV79nmKsjTi54GINARyIHUAyzavYizm53t2z+Hn2YCN+LEu+uky3RVSVxrHlgU7Nax/Fbo+Lh5Au1MMgGfLpNM1m95S9oA2z81QTwRM8h93e5Fx9KoruOSlaROV5N9cmg+NLigXK/NIsK478bl/f7L9l8Y0npIMWXoZrq9HFsHdU/PD+gVWw5b0XGRStkt+KWXYNcuWLbMTMDgcJgHdaElxYuiL4KkzbDkihOuu4jQVuYhpCvV3Ge9eF/zDh+GmTNN/aVIt61iyzXYTKhxcB40OJ9qWW8rKLytuUaueRwi+5vfSxJQF4KawK4vzUPJqsreF4HEVeZhfU6Guc6Ht4XNb5h6aMH2XJvbPeOMee4DJdwD3n4bRoyAK6+EwEDzVSLLZt7/pjeg5Q2ewaE96p1R/pNqbPsALljqfR0rbffI+v1hy9vQ5lZTh6zEh+4FbdtmAmlHj4LdDk2amABbvXo+/PPhv2HFXXDub2afW3ZzrfcLr/ByVxWfgj2WZQ0B3gTswEcicjKPdaqnw3/BaXfmn7gnM95Es9Gw5jE47XaTlpdbsW1zuxlgrixpvznZkLwRso8BYrqN1WrvW//K6mLvLHPTqtPVpHq6Mkyjvutz3tHqOt3g2Jrin650egLaj8+/MNXuUvYLYdJGWDLaDF7c/gFz4c1t9BUaO2bQILj/fnPDDg01E/fk5JiLzOXTL89bbtz34xjSegjRYdGmwbMvv/G2qvndsOdpbulxCw/2eZDQ50PZm7IXt7ixlXWsmoYXwroJ3q/FL/Qe7Lg6yw3+rX4UzvnBnI/D9/PZ7eP4v93vMWuW+bz9/EwWTlhY2SdPGjrUBIwOHIAPPe3obYmme92IdiMY3m44V8+8mmMZnvzbxpfAod/Mza1wdwRfNLkMtr0Hm14tW8EL+uMqaHEdNL3c+9xIXGPGcGpymQk0FRxw6JwfATENK3dW2W9wkX1Ng2LzG3DaXVU780tB9gDz5CzhDxOoK3zAbHzRVFwizoS0PcWuojyMH28q+Q0L9aB87c/XSS/wNG/W5llsObyF0yJOK7KO+NR47vzpzrzfR04bSepjqTgq8/xuehmsfsQEC/tOMVmxJYkeYhr2iy+DHm8c/ylggwYlj1nma1fWipCw2DROLZv3A5qKDPTkvt/c62EJ2RKZmfDqq7BjB7RrZ7qkHj5sKsDDhsHllxf7byftx60/kuXZh84cE+R7euHT/Hrtr76toMEgWHaLGYjfHlponIccWHgxnP09BNQxL0X2NV2nCn7W3V8s3Wefvs+Mt+NK9Txkc0DtTp6gUoFrwdYPYP8PcOYnZswOcZsMkxV3md9zVaexyHzlX8fUq5bdCoP/BIev/dBP7M2/3yQhPQE/mx9Ot5NR34wi+ZFkbIWvs44g85mvuB3Omwf2kJK7lUX2hVUPlVBv8S1g/NFHpnt2cIH2bImBHjB1p20fgOskpkr3C4NanWDLf6HDw+b4LCGglpRkHlY5fL1kRw82DyyWjoburxSdBam6sSwzhs2OyWYq+hNpPhY2vFT6mU3LS/Jm+ONqM6ZlwyGmTZWdaNonsXfB/tnQ8JL8a4U9ML+L2nGuBdu2QevWJwjyFNTyOtOV9de+3gN7p+813aZOsL0TcqV7Znbz8zxQL3AvO5n1trze9KyYfyH0etcMXF+J3G647jpTPRg/Pj+4s3Wrb51xAFNXbXGtGTOuYDsyJgaeeqro8hMnmr95FSTHZAQeXWnu0xaAZa63UQOrXdvrhM0ly7LswDvAhUAHYKxlWTV2nvH18esJey6M0OdCWXdonZmhaP8PxUQnS/EkssW1Juj+c29YOxFWjy+6jtKOPyJixghY95RJ5Ys403QFCYyq/hf9ghL+MIGwfl9Bhweh+ZXQ+j/m5lU4LTGgHkSdZ963K91cdC/xdLYOOy0/aPZtA/jaUfaxXNY9ZW440ReYC6BlL+bpjNG8OZx5pgkQbNxoAg/r1sGiXYuKdMW6csaV5odW4/IrV47QvIF/20e2J8Q/hLpBdcnOyWbrkZI6lJdC7Y7e/dptAaYS8td/zGfpTM3/LKuzI8tMACN1JySuZMaykQy+KJCgIDPOTlCQGeyuPGbJbt0aunTJv0FYVv7sW8PbDWds57E4bA72pezjSPoRc2zmBnJPJtja5hbvxgOUPa0+bZcZDyj3uP22AXztB5teN42cn3vAijth3nn5/2MPMpXS75rB9FplP4/8a0NLz7Vq5b0msFtV3E4ThMhMMN/b3G4aD65kcx4M25W/rOSYz+WHTvBjpworUt26EB8PWYXiHVPXTgWgSXgT6gWZGsvkNZOLXcd9v9zn9XtWThav/PFK+Rf2eIIbm+BY5kET8Fk8suRl/cKgyeXmXrXsZlN5hirp4peZacZLOHYM0tLMrdXlMun2CQn5mYJF1O2RPwZF7nm/Nsbcx6dVbUDzxhtN4Pudd+CBB+COO0zg+vPPyz/QAzB3u8l4HNV+FM+c+wwAa+PX+t6dMCgKos6FhcMhbYfpLj/CM05Fq5vMuflbf3MuAgRE5ge6cutO34T6fq3660bT3a7xJeYe33wstLjKPHAq/GBl/fOma0ZAhDlu57SFeeebh2x7ppnrqJcalqXWaaKZEWtOR1g0vNxWm3v9cXoy/NKcafy6o4TgX9dnTXeyuX1g85vmHl+c8NNMZv2yWzx1wLRS11s6djTdpNJ9Haao/oCi2WKluU51fMxkZuyZbh4WljC2X4sW5iHV3LnmOpQrtaTkbkeIydYVN6y4A2LvKX3ZKlub24rWjUoqb8vrizaIK/O9xd5jrj1NR5u2hyPE3OOaX2kyV5deCYtH5I+5ueMzczx6ZZ4VvRaMGAG//mruKbm9047bS63JSFP5zIw33aTB1NFWPujZXoH23u3uot18i+vqmx4HS8bC5ncgZYupC6XvNV3tNr5ctvZu9IWmfIf/gJ+6mUl1KtG6dTBvHrzyCkRGmraAzWbGRwqv5WLCvAlYT1mMmT6GtOwSso5C25gxTt2eG3/ufb39K/ljpuYSKRroETfMaWfqkG3vNt36Wt8Mbe8y15NqFugB3zJ7egPbRGQHgGVZXwOXAhtKs6EFrz9At0ED8A+tk/fahmXb8U9bS6tzhmMVCMk9MHk8bTp34pyW/XHmuFgSv4bQvUH0IoW251+OVaClt3TqN3QZ2Iuw6PxuEX+u+oflO7/l+qH3Ex4QSlJ2Kr/u+5vnF33AJvKfFHZ5vwvdZCD/V+8IzV03Yu/8KAxZAbOa8O2y4bSo/QtRjfIrdQn7E6n16pM0/9277/i7Az+m/qAXGNHsPmwbX0awcSS5Hqz8P+oFtcB22k15yy7ZfCbN5r1LvVad816LSzjMtB9e4vorHqNucG1EhIUHYhm040vsw7dhBTc2T0K+awlZ8aRlBrO5yXyOpYbhdtuxbG7WbPmSqEb7GN73BiybjYPpR4g9vJFBR34gq/HdbD3YlqRkOyHBbro2mEdtdyw77Lexc28YTpdFVGQ23XgEq/lYdhzpxKZtQdSr48IvNZbgnM00PfNir7Bp+so32Zw5muzgTrjddmw2oR7LaR08G3fLW1ixNoL0DBsdmh6mfmoaQak7sQKjzA18/QvmqXpYW6T3/1i5NpSEI3706JLKlLnjuanDGAJ/OB17o0FIVgI7DrWg0bIJBARGY0Wdk39MbexH+78/J6xB/r7ftTeAV98I4pobalHHfxd2ScVlq0Ng2t/Y/MNp3L4tfywPx9/fTfuIftTe+TmO5ldhBdSFdc+YciF8//14zu96MQDbk/ex8+gm3h2zmrMfnUavnn5YNjtucdF0wrUA3NNxDGdEduLKBU+wYOvfXDx4JXfefgbnuoPxIwWny8HKfbFYWDTNTiN992JaBNcnMe0om99+gOir7iY3vyJ992Km//IT+O/jsgH/wWazEZ+RyF+H1rJ5wc+MuOgGWtc/DRFhyaE1bIxtyYG1zbjm4kdob92MJS4S/Qfx7oK3ebhrbxxHumGP6o87dSdrd3fO20Yut7h584vnGHzOUDo07Q7AqiObiVsXTxvnQU47bxSWzex7EeGNKS9zbp+z6drSPL1Zd3Q7O9cfoHXKHtoPGeN9fk6bSae+HanVOD9bwcrYi3PzZPw638+G7fU5lOBHp3amZrYq5Sa6uP+HfV9L3Nh4alQXrvnvefTuHUy3bhZOp2nkfPjxA7RtHkK/jheY/Z6yn8Wb13J00yJuuPwxwgNCcYub3/evYPPSdbRsFspFva8Ay2Jnyn62Ht3IeQlzef3RFzlvTB8yMu0EBLhYunMxFhZt3E4y9yylZVhDthw5yPBLNvLIXZ3ob+9CWM4yMiWCLz5/Hj+/HYw68yrsNjs7kuP4M34drZZuoccV48gNZabvXswv8+uwbJnFneePoUHOV1i4yHI0Y9nOC+m+6UccQflPXL+bN4805wbGXHA7NpuNFGc6C/avYOOC37n0/Mtp28g0iFYe3kT84WsY+sdt+Pf7BCt33CbgiW8mMWJsJN1y7sO+4zPcBLD9YBtgK5K8CSu0Zd4NbcGG/nSK/ZrgiPwUYit9F9lbvkLa3cc/W+qTlGynRdMs2jvewQptTkrI+fy5IozQkBwi6zn55OOrefL83wjYMRX79o8RYNHmCzht6SfUatwmb72H9h0j7o9v6D7qRq/j5KMZk2naLIRBPS4Dy2Jr0l7W795Jw7Vr6TX2Nix7fiPPfuhn0pOO4ddqDH/Fmu5nPRv/Ru3kr2DwMq+K+/9teIheafNoPLsr9mbDwOaHK8fOrZ+8zyP3xtMi/VnsqdvJsYKZHTuMbdYtDIz6B3/3fixx47TVY++Cj+h/5QgCQvKzCr797Vey2cYV596CzWbjWFYKiw+uYuOCuQy7YAztGpp99MehtYy/K4tbbryV194MZOhQG/7+sGD1DmIPxmKzbKwa9gkbj+2k/5yb+fOXn1m1K5XTzr0s75w7kpnEN54BcX8a8gaH0hO5ftFTzPjxc4JiF3H9xQ/g5/AnKyeb3/cvZ8OCpfTp1ZM+7c7NO5f3bI6n5eGddLr4aq/P3bbrczJtDQlpeQF/LA+nVriLiKA9HFg+k47Dxnk1jD/4rB5BticY12Y1fnIYwUF8Uj12HutO1wLXE4DPp0WQkXwHt3VeRAB7EWxsz+jMl3trc+3GmUQHR5Cd4+T3/cuxPXoTXVoGE925L8EtTMV6z5rZfD7zOa5aP4Oo0PrmnnhwJW2ytxGRngEtryd2TW1cORY9u6YQse9hkhrcz5odLUlNsxMU6GbCM7W54c6mjBuX/5R1zRozcOauXVAnvyrCP3O+oqHfUmr1vqPAu/Dn97UDOdd+PvZer2LVagctr+P7OVM5VLcVY3ctxGaZ83PRgZVMn/c1a0OPEugXRA5u/G0OrrZfxABHtle9RUR4a8or9D+jD6e3MWPArD26jd0bDtIqaTftLxxL7pGWvnsxS6d9R6c+7anVJP8a2r5BKBv+6YiIH8nJ5jix2eDPz96ibacwwhu1zn9v23Yyb9kn3DjqMYL9g3CLm9/ilvPbot+JDdyKX1AYDpudVGcGA4N7M/RABj3G3IZlN+V1i5tPl/0PgHtaXkD3iHa86B9Keno8e77pQmiHl9iyJ5pjyQ5qhbuI3fExteulMuKs67DZ7GxL3sufBzaz4v1HeGvUaPzndMceWIuczCTmJExl14FLuLHJrwS5t2DhAoR961bRsM472Nvflfc+Vuw4ndptzqZhoWONdU/jbjKWrIB2rFgTSvMmWUTv/ouQPs9gt/mbOseG53Gvf4EXN3dm8IW35Z2fsYc3kp10IWevehpH/8+wHMF518X3f7+Z/3A39vg/sDU4B+n4GFbsPZ6Bcxezdn0wB+L96dUtlbVzptGpfzeCo/IfuOzadJDUrb/ScehV3vfEb2bT8czW1G7aPu+1uJ1HOLx6Fl0uvcFr2T+/nctpXaOo16pr3mubd+3lpz/e57qRD1MnsBZOt4vf45bjWHaMrh0jiGzbM6+udvSYg4cfa8LL1w+ngWsW9kPzEGDSto789kY77EEh+NkcpDrT6RDckha7E7jm8oeIDIlARFh0cBUr/lhFVD0XY/qPw2H3Y0vSHj7f/CNxKXHU8Q9jz9jZfLntZ25b+gI/z7uJ3lvPxd7mepavrofTZdGjSyoTn23II5deTqOk6dhX3kcOQUzf0YGs6PaM2rUIy7JIyk5lycHV/D3tGZ654Fr8DvfwqrcENO5Hs0L7/qXns+nXJ4tmrUKx2QSweHj0fq6490LS08O47jobwcGwaZOb3xdeyzm9e9K5qblPrju6ndj1Dv75sRu3jHmeTtaN2CQDl60OSw/eR8flUwipnz+kw+4t8SRt+JHOw671Oj/f/qAdlzQ7j7Z/34k99l7Ek5luBUVD/GK27Qxk+65AunVMY9rEpVx0ze3ce38AQy+24XLBzKkHeXbsJGg2ls3bgtixO5D2bdJZsDScnMxbubL5AgLZjWBjc0YHZu2J4rotP1I7IAwRYcGBlWQvP0rn5oE07NIvb99nZThZ9c279LryNux++dPcf/XjbAJCEhh59g1gWRxMP8Ifh9awc+EvjLn0ThrVMTPj/RW/jhV/riU85BhjB96Mn92PfWmHWHRgNfuXLmbEkGtpFWWuScvi17M6thH7Yltw/4VXEJXzNRbZuGz1+GP3NXT75zv8w/Lvy3/FhjF7toN7ht1Ok4x3sZFJNrV4bccABi99h3aNuwCe6+LaA7TJiqPtBVd4n0dfTafLOT0Ii26Zv941G1i5bQbXXfIAIf7BpLsy+T1uGVuWrKD/6d3o3d7cE7ck7SH+UDMGZk3D0WQklj0A/nkWNr7If3+9n5YDpjE44GLsB3/DjYP9Rxuxd0ckvTkHW5fHsQbMhl96s2hTP1ot+pA6zfLP5UGdkphKABcOGcikp+00aWKxc6ewfVcM0RHpnN99GAD70g7x975dzH59CA/f8indXVdjlwxyrGC+tw7Sb1d/6qZfgL3VVdDlaVx/3orbZcefQt2nT4ecazqTuWMBy1bXJdtp0a/ZDEKyEqDtHSZYNKsxZB5i3vpz6NziR+pmJmI/7Wbo/y2uuX15eXNHhq34lBaR5r6xImEDscs3EGKL48pBd2Gz2Uh1prPwwEp+/XAIwwd8zOCQ0dglnRwriPV7TsMe2YsWhc5Pa/PbOGufCfXO5K/YMOpHOAlybSFr70JanzfGq37x5pTX6duzBz3bmq7BGxJ3snVjHC0P7/CqtzQNsogMacLUqY0ZOdJOVpY55OeumMXli0fkrW/ahmlM2zCNyX63MvCMDtRr3S3vb4cyB+KIvZmG1lPYO95vhnH55ykWr+9GiwXvU7dFR3ITAw+s+oXM+++jxYIC4/JakPVxMH6BjbCJy7yw/nky17zO8p1ncPr1k7AKZAzF/raSqDpJND79HK929N+T36TH6BtxBObXLVctWEe9oAM06X2BV1wkef92Nq9LYsDN93BSROS4X8AoTNet3N+vAf5bzHI3AyuAFU2bNpUiRubFy/K+5l8xQOTGnkVeJ6bo1wP3ni2yZIxZV1SUSL0QkSmYr9zXQKReiIyd0KjYdTR8HOl9I/JrC+S3FsiZ/0HCLx0uHzZ8UqQHIrcj8hginxRYb641E2XVc11Evm9dpAwT/R4w226GyH2I3ImsqdNZXgp5QqQ/Inch8rhd5FMkflDtIu/30QtChYnItcPN70ubIExEXm40RmSIv8ib54pMv0FkSpB8e++l0q9f0Y+38SMOIQZZ0sSso+84s47H2z4tgwaky5yZ2bI61ikLf8+Wrx+6X85qukUmPJolfy5xyoq/nfLRe1mye0gT6cky6dUlXcaNTZXhg1Llv9feLrLyQa/3vOrZrtKsWdEyZK54Wm5tN016dcuS11/IkI/ezZTbb8yUF0IeFhmIyHMdRD4ZJPLeAJHJyN7LB0gv/pYQUiScRAkiTSYyUcQPkfMRGW8+uy8aXCVT2j4schEiD/iLTKoj8qlnH2Uc8tr3Pz08SDbd11nk9+Eiu2eLHFwqsvULyRqBzGSY1OGIhHNMQkmWaPbJh03vE7kEkWdbi7zTQ3I+s8xuiUH+bIQcCUSCH0NOexaRUX6SSrA8yjNyPnNlAhPl2lsDZdTlSKbdfO5v9Ub6jI4w7wNEmiJyP+JujYy9NUQuH5W/7FedkAHXWt7HsOeYahQTIMQgC5uZZc+9Nv84DnwM+akV8nQ/z2unfyjzB3i2NxSR2xAJxJQhEPP+HkHkJmT+tQOE3EPbs72FH5ljpeXdZlvpDsTxJDL+vgEiS6/yWnblx2bZqAfNsk4bEvg4ctM9/UTmDy3+/MxO8dpHMgVZO/R8acUWaVwnWc7qnCRhfulCjOec6IjIg4jcgUg/5PCIVvIK98nVdb6Xq5ovlisiPhNrIuL3JJJtM//T9g4k6pG6Qgxy61Dz2rftzOdT73HLnBuNkWMBSNgjSLNnEBlpiYD8xGAZzI8yxxosY24LkjGX5a/3065I3yvD8vdnFCL3ItIcafCovdjrTHH7c+xZU2TJ+RNFHIiM9by32oWWjYoSWTNR6k30F9tEZFlDU4bx55nPnIlIh9vNa8n+iH0CQv9J8ku/B0RGIPJwoEhMbZFPyS9vV89neT3ymv1eU77hiDwZIfJC0/zzqNA1NOcLSx7v9JZ0Oi1TXnranMv33ZEpMhL5krESzrG886gRe+T9Bp7zdgwiDyMyEEn/NFAkeYvXZ7Hm+c4is5p5veb+Mv+zm9IJWdoYsU1A2j/QUmRaWJHP8tAw5A3ulhBSpFvrZOnWOlnaRf4jq++7QGTBGyIrPxf5ZYS5NodfL2Jhru/3I/IQkvmOf/7nc7anvMOQmK5PSPaM3iI7Z4ik7BFJ3SfyTZ1i92fERH+xCuyjGy/x7KMY831KJ+TJc8zvr/wXSbz2PHmWR2Q438oF/CxXB78i5z2AvNCX3KGy5fpLkbduGiDyx3Ve20v+Ahn0APLEwPxlb74YaT2+tjAR+aSbef2VswqUIQa57HJk5BXm59H3nCUyt2/R83Mkkom/DGCehJIsQaTJS50fEJnTseh9zj7efGYNELnbXE/Wt28v8m100WUD7jbLRniO9VuRb3rXL/Z8WfFiO5G9s7zW8f4bgUIMMuB6895+bmWW3TDUT27mfWnXIlPuvy1NHrwzTbp1zJLRTb+Re+/MljUrnbJvb45s3uiSF8e9Kc+NWyvzvtwnv089KL9/dVBmvHdAHuz8jnz7/EaZO3m/zP3igPzy+X6Z9sD9Isvv8rrfyxTPedQCkas8dYO7kbYTQoUYZGY7U7a7hxRfZyEGGX930XrLtk/N38IeRVwWkupnrrfj7uknMm9Q8dfQrGNFrqHLhgyRJ0NekfFnL5XxQ1bJAz3nS/Zkh0jKDq91DJ9UR5iIPNPflHdWW3Oc1H28aHk7399aZHpdr//P+RK54nZ/uXKkKa+AvNMLGXUz8kaXu6RLy2R56+UMmTktS95+JUMCnyzms3gsKP/ePsJzP7oSmVh3rHlPoeZ4krsRiUZirCdFeiJyhyUSEyZyn10WP9lH5J/nij2G5zNAanNUwjkmQaRJf7/5svqay0RiOotMHSPyzdWy4iNTR4p6wJxDe8JN2Rz9J8ivA+8UGW2JPNdS5PUOIp9Zprzh5togj3rqcZ8gaVd3k9F8JcGk5l0HZQoiOdle+2jJhD4ivw4ofn8Wqrcsm9RD5Mr6ReqGKR+HiBzb6LWOh56OEmKQ3v9B1kQht15k3sfutxuKxC/1WnbTy6fJs36ea92FnmvdIKTdw0XvXcGPBwkxyKjLzbb/bGxej37EVnR/TkDOfhD5vEv+/WjEaGTVM5Y81H2aNGuQIbdelyZ335Qm3TpkFr3ejkHqPukntolIbHSB82hiyfWWvPOz4Gf5TW3xMiNKZAqSek1v+ZgbZAQzZCC/ydUN7hUmIg0e8K63cPFNMqWNp2zneu4R0cXXW/6IOVPkl7OKXuuYYJY53dxf5E5EzkHco+vKc4yXINIknEQJJUnShiOHiJTXuFcu4GcZymyZ2exSSR1RT4YxM2/ZINJkYuCdZr11MW2TW5H7Lw0TJprrv4D80RixJiJ7z4socuwkBYUWe+/Krbf83cgs1+8Gs0+ticjw0ea1/aGmftFwQlCJ17Zut5hld9XynEc93pZfNogM5QAAIABJREFU+3rqOFcgco+nvlRMHeeJ4ZPk+9Mnitg89ZH7kF97mjK0vTP/PhfwOHLfvWeLXN++yPsrrt4ybKI5N4ZchSxuilxwtSmbbSJS7+H8/210P2KNvkSmdrxL5DKbyIsdRN7tLTLZJhOtx8xyzU255FZkcWhfeb3WRJF2nuuUp50Y906UyOG/i3zG7sv8ZR7nyDg+koH8Jjda74r/BPP+9oeaMnS/BeH+6Pxzo4+nrjbAU2+xI3ImIg+YcybzXb9i96dMQd4f8JbUdiTLgB4pcv5ZKVIvJE0+73y1yP3tRL66VmRqkMgU5PqzP5GF5z4ucpbnvT2CfP+2Oe+6e/ZnfLApZ8SEQLEmIgs8bZCHzy9wfoJIB0/ZxiDzhxett+RemxfRTyKIl3COSTCp8vz5D4ssuMRr2S2ee2KtR8x+z7GQ0EdLrrc4h9tlJpfKLbW/klu7/ym3dVoo1593vRCDxJyNrIhG/tvT3FcT/xcukrjOax3LJvWUV8OfFOmNaS959uf2N5uIHFrkteyuN5uIfOVfpAzP1rtOZIAl8nwPkSmXi0y5XBLfq1tsrGPbOQ2KHr9Q7LJL7zpT5I5BRZe9uokUB1ghJ4jjiFlD+QR7Cn716NGj2EKVRnp2uvy45UdZuGuhOHOc5sWJE4t+ABMn5r7jvC+32y0jvh7hdWEa+39jy1ymijJvxzwhBmn9lgkkvfHnG6aCPn10kWW3bBEZN05k6lSRRYtEli8XWbJEZOLvTwsxyJn/O1Ni5seYC9yoK2XwYLekpXmvo2VLkYULRdLT81/LyBBp0kTk1VdFUlNFkpJEkpNF1qwp8I+5N5EkkUmTRN59V2T6dJEffhD5/nuRIUNEbrlFJC1NJCfH/EtqavHvOStLpE0bkX79RNauFTl2TOR//zvJD7Dg8bD8LpGVD5sbda4ZUbLy5fMkOFjk5ptFdu0S+ecfkUGDjrO6Qje2M/935kkW7uQ8vdDsz3M+O0feX/6+VzkKl23J7iWlWjcU+AEkJStF7E+ZCkBqVqos3bNUiEGav9Hc+59AslxZEvC0CUTFp8bL2oNrhRgk8qVIcbvdXssW2ajnKz5epHZtkRdfNMdgUpJIYqL5zH3hdrsl7LkwsT1lk82HN4vb7ZaApwPEijFBnaavmWDzuFnjhBjk0q8uLfKZnf7+6aX6zEoqx61zbs1bZ/Qr0bI8bvnJrazAZ9bxnY5ie8om3236TkRERk8fnbcN+1N2yXZly8JdC4UYpNWbrUq3meN9xgX20euvi4wcac5fp+fym5pqrjlBQSL33iuyZ4/I6tUiZ59duvdX3GuTV08uso8umnJRsct+8YVI8+Yiq1aZMqWliaxcWYoPoSRfBYik7hFxey5enoZDXgWrQBkGfjZQiEEmr54sIiI9PughxCBnfHhGkfexcOfCkytPcZ9ZAc8tek6IQcZ9N05ERIZOGWqCnbNvLVKGOZvnlLjeK680+zT35eBg38tQmvKKiLy77F2vcg36fJDk5H7eBaRmpYrjKdM4H/LFEIl62VTiB16xTq680uzz9HRz33rxRZGrrhLJzPReR3a2b8Uurdz76x0/3CEiIm3fbivEIPN2zDv+Pxaon7R6s5UQgzy76FkZ/+t43+4xBe9zvvAs+86yd4QYZNhXw0Qk/97yn+/+49v2SjBrlkj//iIpKfmftdMp4spxeV1z67xQR5bFLfOtzL4qULbdu80xGxxs6iBNmxY6hj3cbrc0fa1p3mfR68NeQgxyw6wbSrXp664z58vNN4u89ZZI587FlOs417oTLlsSz7KZzkwJeqZoIzzpkfuKb1Dk1pFPYPWB1SYY9nKUiIi8tOQlIQa5fub18uXaLyX02VCxYiy5YdYNkpCaUOw6Jk0SGTzYHBMpKfnX5+J0ebeL2J6yycyNM0VEZMgXQ8SKseTdZe/69DmI2y1yJFZk/68icT+LxP2UH5yfYvP6l4L1loS0hOLrLcfblq/7qIDJk81x2Lq1yOjRIhERJS87dKhI+/Yi8+ebevfs2cUv992m74QYpPv73UVE8uqGl0y95PiFKfAeHpr7kBCDjPx6pEz7Z1re8WN7yibd3u8mIiLL45aLY5JD6r1YT87+5Gyv4+z95e/n1RdTslLk45UfCzHIxVMv9rkMxdlyeIs4Jjkk9NlQERFJSEsQ+1N2cUxy5LcBj7dekOTM5Lx6YO6XFWOJY5JDbE/ZxJnjFLfbLX6T/MRvkp/sS9p3/DKXxgneX27dfcaGGZLlyhLbUyaIejjtcJk2+3//Z9p0GzaYuvSxYyIJxZ+exYpPjc+rWzpznDJn8xzzMOK5MCEG+V+saZgN/mKwEIN8sOKD46+wwOewd69ISIhIZKSpMw4bJhIQUHTZ/2/vzMOjqs6A/ztZSUJCiECAAGFXwr6DG6AIKO5SXFDBpdaFuvRzQ1Ri+VpFca1V+omtimtrqVXbWqviDoggAqJsgqyFKiigEkNyvj/eeyc3ycxkJplJMpf39zx5cufOXc4795x7znnPu5SVl9ncu3Itxdi1X6+1q3aushRjW9zdIvy8IlpicY1GRqTKnpQIjH+2Ae09n9s5++JKRmoGJ3Y7sfLO4uLqvnNBMMYw/+z5rNy5kktfvpSnzniKw1uEiQzfwAxvL9mH1u9ez8fbP+bltS8DcOrhp1Y7tls3ePzx6tfouPci7njvNhZtW8SibYsAKMzpTPIP1SNWlZRI7BNv3JN//lNimFx2mQSRczk8yM+WkwO33VZ9/5VXwsyZlQPjZYWIpzh/vmSk+de/KoLnnX9+8GNDUlxcPZjWvN/B5fdWBCFbUQwHdnLDE89x/vkS1NK93/z5oS/dpmkbduzfEfj89JlPR1m4unFRv4u4bcFtvL3p7UCsnyNaHMGbk9+k/X3t2X1AgqMWjyzmqA51SwfbNK0pvVv1ZvnO5cz6YBZf7vkSgNMOP63asWnJaQwpGMJ7m9/jgUUPsPtHKce4ruMwbdpUDrRqTNAgq489Jumnr7xS6mFEmSk8GGPo27ov729+nzVfryErNYuSshJy0nIoKSth897NpM1MC8QSeGDMAyzdvpSt+7YGrhGL52mM4dHxj/LguAfZ/9N+8jJikyWmU24nPvvfZ+zcL7/ltr3bMBhaZrZk1w+7+Me6f7Boi7Tx8d3Hx+SeVXnqKWle3vablQXXXgtXXy3tPytLMiD8859RXrxqQF5juDA/n2dnj+XfG/4NQHZaNi9MeKHaqdbCrbfCI49IbAY3M16vWITc6XC2ZFXseZMEiZfFDYKFthvVcRQLNi1g1a5VAGzYswGAFya8wIS/TODjHZK69aFxD3FsxwgzFkXJmC5juOWtW3jzyzdZvHUxC7csBODGo27k4v4XM2TuEACWXbaM/m36B73Gli3yHszNlX5l2za4+ea4FBeAKwZfwUX9L+KBhQ9wRo8zQvbLWWlZHFN4DAs2LeC1Da/JznLD+3/rydatlfuY0aNh3jyJv9OsmcRTc62fm8UgW21VJhRNoPidYuYum8tH2z5izTdrSDbJHFN4TETnG2MY13Ucv1/ye6a/NT2w//QjYhdPxcvQgqGAxCsEWLp9KQAjCkfU6br/+Y9kqUxLq2iHEnw2mZfOeYk9P+5h1/e74j72uuUWiYv13nsSrDMpCc49t/pxxhgmFE3gvkX38fKalwP7Lx8UeYDYtWvhz3+Wv1GjpB5efHHN58WS9JR03rzwTY78Y0UGo9+N+x05Q6fCnffV+rp98vuQnZrNzu93ctYLZ/HBlg8Aqe/ju49nUu8QcXY8zJ4Ny5bVEBDZoUfLHqzYtYJte2U6sWXvFiyWbod1q+FMB2MkgLqXnwUPGp+WnMbggsG8v/l9HlnyCN/8+A0AY7uMreQqEStKSyWW1gknwDPPSMyW0hDJC1euhMWL5X+rVlJ/xwVJagZwZHt55mu+WYO1NtDPjOkyJuKyTek7hXs+vIf5X8xn/hcyCO6U24lN325i1/5dAOzYt4Oy8jIKcwt556J3+GzXZ/xr/b+4eujVpCWn8fCSh1m1axV5s/IoczLW/ayobsHCOjWXcAz7S/fzQ+kPbP5uM2W2jPbZ7SNORJCdns2CKQsY+cTIwL53L3qXMfPGcODgAXZ9v4uMlAwOlh8kJSlFEqnUExN7TmTRtkUUv13MMyueodyW0y2vG4dlRpI+KjTTp8u8pmvX6pmCI6FlVku6NO/Chj0bmPH2jED/MLhgMG9tfIsvvv4CgHW7JXZWv9b9Ql4rWNnatYOFC2Wcf/CgZCWtSpJJ4oTOJ/CX1X/hgr9dQLkTI29c13FxaZ+HIpGEOV0CdDPGdDLGpAHnAC/XcE79UVxcPQS3MVBcTO/83iz++eJGregBSWXqThRHPjGSBRsXAHB0+6MjvkZBTgFju0gk9+ZNJBjB3397NkVFMHGiDMxWr4ZFi2DoUFHqzJ0rg5cvv5TvW7aUgbKX9CgSiY0YIcobb8C5qkFJXebNgwsuqHz9iKPXuxQXV1/HunOrpHj98k9Q8jUU3cQPJ2zh/fWjmDGj8iAklCIKYOElCwPbL/7sRbrkdQl9cBwoyClgcJvBAIHgrQ+Ne4jM1ExWXSkTzK55XZkxYkZM7texeUcA7v7gbp534oN0z6ueFQgqOuXZC2fz+CeieezSPPLf59VX4eyzIxsQhmJEB5movLL2FZ5dKYFu++T3YWAb8cd3s5tlpGTQMa8jH17yYeDcp898mh4texAr0pLTYqboAQLvq//uFyXZzu93YrG0zZZ0Tuf+9VzuXSQZvXq0iJ0cXkaNkneCN5Dkzp3w2WcSCNbbdqJV1oXi7+dUpLVffvlymqZVryB79ojucMyYygOb2gxyqjH8CcmGseLXsOIOCdR4+K/gi2mV+xhjGHaxZL57bf1rPLviWb498C1ZqVl0yO3Auxe9S1pyGsd3Op5fDv1l8HvFAFeB89V3XzFm3hh2H9hNskmmU/NODC4YzMarN/LVtV+FVPQAPPmkvIMXLoQJE0QB+2TwGNGhad262u9TLWCkhyYpTbj5mJtr7JdfOuelSp9nj52NMaZasMsBA6TfmTcPrr9elIHTpsGdd4aeYNWFHi16kGSSKCkrYcn2JQAUZBdElRmteGRxtX3XDbuu+oExoE++xL/YsGcDsz+czXubJa7C0HZD63TdCy+U333PHti3T/r6fZ6ERs0zmsd97FVeLsrKhx6CggIJgJuVBU+H0OXPGj2rUubLEzqfwJCCIRHf79VX4eijYeRIuY8x4ccR8WJ4++E8fYYIObrTaKYOnVrDGTVjjKGgmcRtm//FfHZ+Lwr5YzpEpsQEyZQZ6TjOrZebvtsEVPR3XfO6Rny/aOiUK+OW3773Wx5d8igAnfM6hzul1ixZIplan3xS6kd2tijUg/GXv4hyJyurYgE2VMauFpktyEnL4YfSHzjqj0fx/EoZqw1rF0H2K4eiVkV0yJFYRDnpEily5nEzsdjAIuKO/TuwWLofJmPAnq16cv2R15OWLLF/Tu5+cuB67sT8jCMq4qXUhpSkFFo3lX5jy3db2PydZMg8osURUV1nROEInjr9KQCePetZju5wNAU5BVgs2/dtZ/u+7Vgs+U3z654FNwpcpfvKXSsDSrZY1PV16+R9VJcxUPscsee454N7+M8GCbJ+ZDtRLK7ctRJrLVu+2wJAr1aRraxZCy+9JO/mnBx5LzRtKvPRYPRuJfFEF29bHOhX+7TqU2uZArg6gqpjlAgMR/xEjTXdWnsQmAr8G/gc+LO19rN4Fyxigk34rU24B+m+CErLSrFYkk0yHXI71HBWZR4+6WEA9hzYw7gu4+id34t77oHnn5fOY906UcQ8/LCsvgwdKgErXQXQ4sXy03kH09+HCGYe9P4Pw5tvwuWXy/9Fi+CJJ4Ifu26dDJpiMknzklkA41dAVidYPxdWzWTTov/QurUNpOiLhMLcQn4x8BcUtSzirKKzYlzIyPh/p0o+8G9+/IYuzbtwQhcJRtwmuw3lt5ezdura2l+8yotvyn2iYDxYfjCwUnNhvwuDnjql7xQAysrLOOhkCrhkwCUyC6/aDoOkTm7SBPbura5YjIacJjJIeWL5E8x4WxReWWlZzDl5DgAdczsCMHPUTADaN2vP5YMup3PzzpzX67za37gecBVnW/ZK5+paT43tKsrc0rLSwO9+5hFhMiLVgZkzYcMGmdC99RYsXQpz5kia5+QqCXGizowWop6kp6Sz8/qdrPvlOjo3dwbhVRQJqYdlU15SGlKJXCeMgZbDof+dMPA+GHg/DLwXfv1/q5W35yeyGr1q1youe/UyANKT0zHGkJGaQcmtJbxx4RtxKGQFSSaJlpktAfjJCQrqTmhAFLgdmoXvQ155RbI3tWgh7+LMTDgzPlUqanLScwLtF+Dqob/k6qsl89T330tftn+/ZN1p104UPH/4g/RDjzwCd90Vh/4FSEpKCkxs3N97+jHTQ58QRBnWonMvrhpcEQx65qiZpKWkBTm57qQmp9LEyZAz7c1pAauGrs3rNtEYMkR+6/Hj4YorJDvtVVfVfF6dqPJbrk7uRVP2M358ZM86JTmFx055LPDZHTNFyiuvwCmnBFFwV33GUKPSs65M6jOJpT9fyj8m/SNm1/zVsF8BkNtENBNdmncJ9LWRMHmy1ANvJqxQ2aZcRcKG3Rsot+WBeulOPGPN+X3EdPxg+cFA/3lxv/iYZb3/PhxVxeA6VD+5ejUMGiQKoUho1kTMFRduXcj+UvlxI52Au/zhlD8AsLdkL62btmZS70lkpGRw4OABfij9IWBt1atl8OvOOFbGXK6y5PTDTyc7PUIBwnDEYaLY2fzd5oCyZ0CbAeFOCcoFfS+g9LZSzu0lJn7d8sRabNvebWzbJ7J1zo2Poi8UR3Y4MvAebpIi/68ffn2dr5uXJwv2deGS/pcA0jbKncxcF/W9CIC3vnyLwgcKKS0vJSs1i8zUzJDX8bJuncw7R4yoPGYMpQy+ZqgEHk4ySRjJYy59ZJSLSdXwiY6grkQ0TLfW/tNa291a28Va+5t4F+pQ5NZjbgUqVvV/1jN6k8iueV0DDfE3x1c8pqZNZaX+tNPE5L1NG2kvgwbJQP+882DKFFnZHT1aViW++w6++gqmTiXixpabCwsWiOnehg2yWtyuXfC0g02ayIpgXDBJ0HasRFjv91uSul1EeXn0poBzTp7DZ1c2nF7Tay5575h7K31njImpeeOpX4mJlavoGd5uuFhWBHn2o4afS4pJocyWidl1Xjfa5bSL+F7nny9KQG+647CpKYNwUteTAEn7+qOT6vTI9kfSO783GSkZrPlmDQBT+k0JnPPo+EfZcPWGRm8W6k7Qt+zdgrU2MAi++Sjxr3EHe4XNCmnVtFXdbxhkopKZZfjnstb85jewZg288YZ02OXl1ec0saRVVquwq13Z7Gdw6ifMmVNZER2NUjoWtM1pi8FgsXxfKjd3V6rrk5uOugmAvvmSrefWY2+N6vy1ayWNt9fKLimJ6AZYESp5a4M7ADy/z/mkJqdy553QsSP06SNWPDfdJEqH+ubmo6Utum0zlGI8HK5sAJcNvCw2BQuB6xpT5qQNbprWlKSotbTVGTUKPv5YlGxTp4qSrT5ZxgD6D0rixyrZrsNZjbqTfqhQOETK9u1iSVZV4V3JLbXq/qrtKDk5ZoqhAW0HBKwtYoHbX7r1JFrL4XvvFVfQsWPhwQdlgeDSS4Mf6y5qLNq6iJvfkPbUIqMFyUlVf9zYcEJnWSxzxy2FOYUU5hbG5V4ffCALqJEocDIzZSwcqRXiuK7i45XqhCrITssOKA8ixev2dffouwECLk079u1g47cbASoWXarQJLUJE3tOpKRMVl2uGVbLDEFVyEgVLeqYp8dwzWtyzUiVC1XxWlr2bNkTgDe+fIPXN7wOxM8qOhzu73Tg4AGSTBLHdT6uzte8+mpxGdy/X+pQWVllZWsknNtblGLub3ZOz3MC1v4H7cHAwmM0z2LdOujdu/I4H4K8Ox1ymuRwQucTKLflWCyndD+FzLTaPXulOvVnw6aEZVh7McNcv3s9ABf0uaBW15kzfg49WvSolTZ89mxZlZk+HXr0kBW7oiKwNZ8awBg597LL4Lrr5BrBxpTjxonfe6hVn1jSqRN8/bUM1LxEq2BoCFzNv9dsNiZUmZyZ/+7k7J5nB76uyT/fq0QJW1eDKBLOvSSD/y1cx+TJsHGjmH6//350xe+VX7HilJGSUanMrotE2+y2dfaHbghcZc/ab9byt8//BkBekzxyM3LpltctYOnjfQbx4ogjZNX+pptkULFmTfWBRF0stGokiCLhwQ+HcMcd8MAD0q6//hruvz+OZQiBu5rqutd5J5F1IgpFi2t16K6CBovzFoo9e+RZ9qgy5o3GdTfeZKdnY2dY5p0xD5CVwnvvFXfCyZMlNsvChTVcJA647qJ7S/YysuPI8JOtEMowb2ySVlkxUNqG4YpBVwAVq+Ted30syMqShZ26uOZGRJXfcu/vnqJVYWY13Uk4PVZachpbr9vK7huDx3epiaDjhvz84AcHK0iogUcwhZH7LqjLynYUpCanMrrzaPb9JP54J3U7Karzs7LEtW/uXGmrP/4I11zjCX/mwX1v7ti/g3s+vAcgEGcvHhhjKo1VLuhbuzF2JOzfD23bRmb1OnasuEz/9FNk1/7VcLG+GtxW3PyvHHxl1OVLMkkBNx13Ydk6D6nH73vw9ApxEXTdvILxi4G/CGxH4+oXjjZNq8fQca2068Lekr0APLbsMR7+SKz5XKuo+uTMHhVms1cOiv65BePmm8VNatgwiaV4xx1wUnTNluSkZE7pfkqg/U0omkBSUhKZKaJscZVA7qJSJJSWylgiWNsPhbd9BuYgcVxMOpRQZU8jIckkMbLjyICVwrGFtQvqeUHfC1h91epal+OUU2QVf/t2WLUKbrgBTBwa2+TJ8Ne/inuI6+8frTY6UtLTJVDejTdWWACUl1NtNdClsLBifNXQf7PH3gPFlpTk5JhdszDEYtZZPSrc1QLB0UM8e++kMtoBYQYHWJh3MllZokzMzq6d68joTqMBAm3Gjat0Xm9x02rs7lqhcF1zNu7ZyJS/TwHgQJksj3iVcNEEZQxLsGccpI3n5IgVxbRpldtRvNptKAYNgrffFlfR/Hz5e+ut+i0DSBB1kDgKAKcdUT2gea0INukLYTngDoR37N9BZmomzTOaR3ybzZtlQhJUudPIB1hNmsDw4eIKHKn7QywxxnB8p+OBug3ay28vp/z2GK46hFAOnHSuxJhyLTa8k46Q1/CcH093pLqQnCzK5mgmFCAx8aJpKy6dO4vVxsGDVb4I9Q51C+f9C6UYCrW/nnFdOpJNcq0XSw4/XFz6rrtO2mkwa9D8ptXlrcnttK54g6DHK7kBSNByb/yqcJx8MqxYIUkRXOuMcJaqrjXapzs/BeC4TrWzDnnnoncomV4SUFS7ip3ScgklAYS1fHIDvOc2yY2ZNZabrMaLO86rC0UtiwAoKSsJWCMdflj9x3Id1HZQYHtC0YSYXDM1VWLj/OlP8nnfPlEARftO9C4AjO4sv/kp3U8BKhY3Lh0QwkwPqvUbzc4YxY51+0gLZngYoo/xtslqCZqUOqHKnkaEOzlNSUoJGpzUT/TsKYqk8eMlLsi0aWIeHS9mz5YVp4kTZSX4jTfElzQYmzYFH7f55W/TpuByuy94CG2+6zKq06jAdrgAsKEGwc13reHJJ8VdcMcO2LUr7O1qLIM78QJol9OOxZcs5o5Rd0R/0UZA62yZWFlsYIW1Wbq4bnkVPO7KXn3y8MPw3HPi9rlkibSpqrEJ6oMBA0TB89NP8tcQyh43sOvKnSuBCqVPnQk26QszEXSz5tW4wltlgHWg31CaJe+LeEVZqYybfaYug9KI3HGjiQcTQinYYcPXAKz+WhaCwgYljkLZ2NC0bg3r11d3DYhXnT71VInb440ZFrWFcITK9aiPjRGum1BtFxyjwY1h4rokue428cKrGPFOvGNN794SAzMShU+zZuLWfv31stg6fbpkKw1Fkkmif+v+AffhaIIze0lJSqkUJyxYIOSiFkUhz09OSuajSz9i5RUrw98o2MQ+xPvrxK7yLs1Jq7AoapvTNvz1I8DNYualtr9bJaK0vEsySaQny+pKXbPoejEGBg+WOHX33y+eE9G627sxIaEiVMC4bvIucGNcHd0hTNKgKn3EQJbyxcb0aouB4RYHvYlOwlmVKdGjyp5GhDvZjpVJZGPn9tvhxRdh61ax8jnqqPi5hHTrBq+/Dp9/LlY+p59OcI3zIYy70hlJhgKvMrIuGQ3S0iTAXG3CR3iVHV5lD8CQdkNq7evdGEg2lWcvrgXH4IIKmVOT4xB9tgb69oW//12Crx93nMT8ahXKA6UeXBCSk0P7gMcbV8lpsdXqX52I0qrGVfaM71bDSnWVwZjBYsujXP5TAvxi0C+wM2zjWpgJoRQ0+fn0y+/HT2WiBQmrmIxS2diQDBsmKaurKlziEsAdmYx/8gk8/rhYX/zwQ/24otcnuU1yuXbotTw47sG432tER1lxc5WPrlVuUGJgceYGngaiyp4XLSNHyqJiqKxaVTnrLHjnHRkHPfecZMatZj3mwZvmPFaTYjfmnKuMSDbJNcb1GlwwuOZ4jeHiWVXBtfZyFVn9W4dZSIwCt69OSUoJxLgKpgCKmihkc7lv7H2M7TI2rvWvNrh9gjfJgxs31E2/7rpeBqVKH5HDPjolb2bWrMqWaqWlhB3jzDt9Hs+e+WwMJFK8qLKnEeGmtI6VeV9jxxjRQC9ZIsGgH388vhO3I4+UoGEffSQDxA8/rPmcQ411U9ex5botER37zuR3WPLzJXEuUWi8K3N1TSPcYIRY9Wp6QGYv7sqna3KckpRCEkkMK4jBqlQtGT1aYi0tXCiZRN4IlXSqFgOhRMKrTHRXwxuCsV1kRW7IeODHAAARpUlEQVR4u+om8JWoMhjL4Ee+2W0aVYweJQgxsgRxxxWuSX5U92tELnxe2rSRxBB33lkxoSgtlfdTPGjXTlyTpk0TK58bboDu0cV4TgjuH3c/vfN7x/0+rvWQu2AU9p4xsjibdfws5p4yN+rzouGoo2DLFrGEdZWBNSURGDRIXJO3bBHrsXCKItfCosbU1FEoyNzMnu77YVCbGFk+Rem2WNSyKJAopDaxR4ORkpRCdlo2B8sP8lPZTxgMWWlZdb9wLVwyrxx8Ja+d/1rd7x0H7AzLl9dUpPZyg1h/X/o9HXM7hrdCDdJv3PxYV/7wB4nhVVYm4UHG1BB94Py+5wcCRiuxo3GpFhXsDF1pjSfJyRInRglO18MiT8d7bMf4m3mHwxtzIVaDgnonxGD1xDWW5/tIBpFX1r3CxJ4TA9+VzYhnROTISEmBXjVle83PDy5fI7USqA3d87qzdvfa8G4xcaZtTtvI+o0qE/ZO+2Fnc5mM5OWFOEfxDa7bgt8sh6+/XoKStmwpweQ//VRiwFVNyBArfv1riZMxa5a46rSIkffmoYgbf8bN/hQ27Xqw/qQWfcmNR98Y9TnR0qyZxCy6/Xb44guZ4D72WJiFkShxF7r6tq4hYG408d+c7EvflnwLQL82/YIeFzVRKoqHFgxl9f/E3dSbkbau9G3dl/c3vx/YjgmNVAkeK9JTKlaCarQcDsLkyeKieMstoiS3FjIyYlhAJWLUskdRlITHa57tB/o6MYzc4NOxyEhR7zRAvIn6xl2JTkRlY9OmYhWxfHnl/fUdcFupH1wX0JhNdBoJU6fKnP+WWySN9fDhEgsuXiQnQ3GxKJOWL5eYQUrt6Joni0tb924FCB/oN4EszkAUkAMHwvPPwznnxDZrYEZqBpN6T+LGo2pQXEXhkukGat6wewMQPIZPfeBV8MSyDL1b9Q66rYTHjaNVmwWtpCSxVrvhBujQQRJ8fPBBrEuoRIIqexRFSVgGtBkQ8MFOSEIMvDogfvjb9m2Tz3HOUqLUjolFE8lIyWhccVuioKgI5sypHEg02sCOSmKQk57Dxz//mHN7+ctEPj0dli2TdMMZGeJWFcuJdSiaN4euXSOPy6JUx1X2QOziszQWMjPh3XfhgQdEIfn887G9/tNnPk2vVjWY10apIGuX3S6QraqmJB3xwqvgiaWyx83IBdT8uykBXOVbba2sUlJEOb5pk1hC9vdXM08YtJtSFCVhWXrZ0oYuQt0IMfAq3PwB/Olotu8TX4SY+JcrMWdir4lM7DWx5gMbKRMmSPaX1asl+HZZmWTzuPXWhi6ZEg8Gtq0hXk+Ckp0N8+c3dCmUaPEqyWMSMLeRkZQEP/95Q5cicga2HcjWNWJl5Q3UW594FTwF2QUxu26X5l0C214loxIe93drKEsvJTaoZY+iKEojw7Xk+a7ku0B2DEWJNeedJ1lfjjtOVt+uuAJuu62hS6UoyqFCfaVdj4qqgY0h5pkkGyNe9yY3YUx9483uFTYgcJR0yatQ9ngVP0p4rhpyFXcdf1diW9ArquxRFEVpbLTJbhPYVpNjJV4cdpi4GIAEnJ03TwMoKopSf7hKhUZlbeHzTJKh8D6DhnJNdjOzxRpv3MOGclFLRFplteKmo29q6GIodUSVPYqiKI2MlKQKD9tEDP6rJA6zZsG550rsk/R0ePzxhi6RoiiHCl2bi4Kh22HdGrgkSkNZ8wQjJz0n/AFRWl95LVOy07PrWjxFSSg0Zo+iKEojJD05nZKyErrl6SBYiR/GwNy5MHOmBBVt1qyhS6QoyqFCm6ZixRo27bpSL7hxejJTMxu0HHaGrfmgQ9T6SlFqg1r2KIqiNELcuD2FuYUNXBLlUKBNG1X0KIpSv4zqNAqDCZ92vb4JkSUz5H6f0Da7LQCtm/o7NpGiHGqoZY+iKEojpEOzDqzbvY7CZqrsURRFUfzHpD6TmNRnUkMXozJh0pP7GVfhlp3mTzenGSNm8GPpjw1dDEWpd1TZoyiK0ghx0466Fj6KoiiKoijxomfLnkwdMrWhixEXikcWN3QRFKVBUGWPoihKI6RlZksA8pv623RcURRFUZSGZ9WVqxq6CJGRnx88Po/PXe0UpTaoskdRFKURMrDtQCB+qUgVRVEURVESjkPU1U5RaoMqexRFaRQUNivE3GFqPvAQI5a/icb/URRFURRFUZRDA1X2KIrSKNh07aaGLoKiKIqiKIqiKIovUP8ARVEURVEURVEURVEUH6HKHkVRFEVRFEVRFEVRFB+hyh5FURRFURRFURRFURQfocoeRVEURVEURVEURVEUH6HKHkVRFEVRFEVRFEVRFB+hyh5FURRFURRFURRFURQfocoeRVEURVEURVEURVEUH6HKHkVRFEVRFEVRFEVRFB+hyh5FURRFURRFURRFURQfocoeRVEURVEURVEURVEUH2GstbG/qDH/A76qsrsF8HXMb9Y48LNs4G/5VLbExM+ygb/lU9kSEz/LBv6WT2VLTPwsG/hbPpUtMfGzbOBv+Q5F2QqttS1rOjkuyp6gNzLmY2vtoHq5WT3jZ9nA3/KpbImJn2UDf8unsiUmfpYN/C2fypaY+Fk28Ld8Klti4mfZwN/yqWyhUTcuRVEURVEURVEURVEUH6HKHkVRFEVRFEVRFEVRFB9Rn8qe/1eP96pv/Cwb+Fs+lS0x8bNs4G/5VLbExM+ygb/lU9kSEz/LBv6WT2VLTPwsG/hbPpUtBPUWs0dRFEVRFEVRFEVRFEWJP+rGpSiKoiiKoiiKoiiK4iPqRdljjBlnjFljjFlvjLm5Pu4ZL4wxfzTG7DLGrPLsyzPG/McYs87537why1hbjDHtjTELjDGrjTGfGWOucfYnvHzGmCbGmI+MMZ86st3h7O9kjFns1M0XjDFpDV3W2mKMSTbGfGKMedX57CfZNhljVhpjlhtjPnb2JXy9BDDG5BpjXjTGfGGM+dwYM9wPshljDneel/u31xhzrR9kAzDGXOe8S1YZY55z3jF+anPXOLJ9Zoy51tmXkM8umn7bCA85z3CFMWZAw5W8ZkLI9jPnuZUbYwZVOX6aI9saY8zY+i9xdISQ7x7nfbnCGPM3Y0yu57uEkS+EbDMduZYbY143xrR19id8vfR893+MMdYY08L5nPCyGWOKjTHbPP3dSZ7vEqZOQuhnZ4z5pdPuPjPG3O3ZnzDyhXh2L3ie2yZjzHLPd4kuWz9jzCJHto+NMUOc/X5oc32NMQuNzA1eMcbkeL5LpOcW1dy7Vs/OWhvXPyAZ2AB0BtKAT4GieN83jvIcCwwAVnn23Q3c7GzfDMxq6HLWUrY2wABnOxtYCxT5QT7AAE2d7VRgMTAM+DNwjrN/DnBFQ5e1DjL+CngWeNX57CfZNgEtquxL+HrplP1J4FJnOw3I9YtsHhmTgf8ChX6QDSgANgIZzuc/A1P80uaAXsAqIBNIAd4Auibqs4um3wZOAv7l9BnDgMUNXf5ayNYDOBx4Gxjk2V+EjMHSgU7I2Cy5oWWohXxjgBRne5bn2SWUfCFky/FsXw3McbYTvl46+9sD/wa+cvt0P8gGFAPXBzk2oepkGPlGOf1AuvO5VSLKF6peer6/F7jdL7IBrwMnOtsnAW97thO9zS0BRjjbFwMzE/S5RTX3rs2zqw/LniHAemvtl9ban4DngdPq4b5xwVr7LrC7yu7TkAkbzv/T67VQMcJau8Nau8zZ3gd8jkxqEl4+K+x3PqY6fxY4DnjR2Z+QsgEYY9oB44G5zmeDT2QLQ8LXS2NMM6QTexzAWvuTtfZbfCBbFY4HNlhrv8I/sqUAGcaYFEQpsgP/tLkeyADiB2vtQeAd4EwS9NlF2W+fBjzl9BmLgFxjTJv6KWn0BJPNWvu5tXZNkMNPA5631pZYazcC65ExWqMlhHyvO/USYBHQztlOKPlCyLbX8zELGaeAD+qlw/3AjVTIBf6RLRgJVSchpHxXAHdZa0ucY3Y5+xNKvnDPzhk3TwSec3b5QTYLuBYvzYDtzrYf2lx34F1n+z/AWc52oj23aOfeUT+7+lD2FABbPJ+3Ovv8RL61doez/V8gvyELEwuMMR2B/ogFjC/kM+LmtBzYhbwYNgDfegaNiVw3H0AGUOXO58Pwj2wgHdbrxpilxpjLnH1+qJedgP8BfzLigjfXGJOFP2Tzcg4VA6iEl81auw2YDWxGlDzfAUvxT5tbBRxjjDnMGJOJrCS1xwfPzkMoWfw8ZvGjbBcjq5zgE/mMMb8xxmwBJgG3O7sTXjZjzGnANmvtp1W+SnjZHKY6bhV/NBUurn6RrTvSJyw2xrxjjBns7PeLfADHADutteucz36Q7VrgHud9MhuY5uz3g2yfUWE88jNkjAIJLFuEc++o5dMAzTHGio1VQqc4M8Y0Bf4KXFtllSmh5bPWlllr+yGrgEOAIxq4SDHBGHMysMtau7ShyxJHjrbWDgBOBK4yxhzr/TKB62UKYpr6qLW2P/A9Yq4ZIIFlA8BI3JpTgb9U/S5RZXMG8qchyrq2yAr8uAYtVAyx1n6OuMe8DrwGLAfKqhyTkM8uGH6S5VDCGDMdOAg809BliSXW2unW2vaIXFMbujyxwFEa30KF8spvPAp0AfohCwD3NmxxYk4KkIe4jdwA/NmxhPET51KxKOUXrgCuc94n1+FYkfuEi4ErjTFLEfennxq4PHUinnPv+lD2bKNC2wYy0d5WD/etT3a6JlTO/101HN9oMcakIpXtGWvtfGe3b+QDcNxkFgDDEfO3FOerRK2bRwGnGmM2IW6SxwEP4g/ZgIAlhWs6/DdEWeeHerkV2GqtXex8fhFR/vhBNpcTgWXW2p3OZz/INhrYaK39n7W2FJiPtEM/tbnHrbUDrbXHAnsQP3I/PDuXULL4ecziG9mMMVOAk4FJzkAYfCSfwzNUuCYkumxdEOX4p85YpR2wzBjTmsSXDWvtTmdBsRx4jAq3kYSXzWErMN9xHfkIsSJvgU/kc/rtM4EXPLv9INtkZHwCsuDmm3pprf3CWjvGWjsQUdJtcL5KONminHtHLV99KHuWAN2MZClJQ8z5X66H+9YnLyMNCuf/3xuwLLXG0dI/Dnxurb3P81XCy2eMaWmcjB3GmAzgBMQvcgEwwTksIWWz1k6z1raz1nZE2tdb1tpJ+EA2AGNMljEm291GgnOuwgf10lr7X2CLMeZwZ9fxwGp8IJuHqqtlfpBtMzDMGJPpvDfd5+aLNgdgjGnl/O+ADIKfxR/PziWULC8DFzoZL4YB33lMqROdl4FzjDHpxphOQDfgowYuU9QYY8YhbsunWmt/8HyV8PIZY7p5Pp4GfOFsJ3S9tNautNa2stZ2dMYqW5GgpP8lwWWDwGTM5QxkjAI+qJMOLyFBmjHGdEeSSXyNf+QbDXxhrd3q2ecH2bYDI5zt4wDXRc0Pbc4doyQBtyJJMSDBnlst5t7RPztbP5GmT0JWBTcA0+vjnnGU5TnERLMU6awuQeKjvIk0ojeAvIYuZy1lOxoxE1uBmO0vd55dwssH9AE+cWRbRUW0/c7IS2A9ovVOb+iy1lHOkVRk4/KFbI4cnzp/n7nvED/US0eOfsDHTt18CWjuI9mygG+AZp59fpHtDmQitgqYh2R+8EWbc+R7D1FgfQocn8jPLpp+G8lw8XtnvLISTzarxvgXQrYznO0SYCfwb8/x0x3Z1uBkaWnMfyHkW4/ELHDHKXMSUb4Qsv3VeaesAF4BCvxSL6t8v4mKbFwJL5vTB6x0ntvLQJtErJNh5EsDnnbq5jLguESUL1S9BJ4ALg9yfELLhszrliL9+GJgoHOsH9rcNYhuYS1wF2AS9LlFNfeuzbMzzomKoiiKoiiKoiiKoiiKD9AAzYqiKIqiKIqiKIqiKD5ClT2KoiiKoiiKoiiKoig+QpU9iqIoiqIoiqIoiqIoPkKVPYqiKIqiKIqiKIqiKD5ClT2KoiiKoiiKoiiKoig+QpU9iqIoiqIoiqIoiqIoPkKVPYqiKIqiKIqiKIqiKD5ClT2KoiiKoiiKoiiKoig+4v8DaJLo+CkTVPwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rescale_conv_revealcancel_fc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rescale_conv_revealcancel_fc_multiref_20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for task 1 for example 197\n",
            "grad_times_inp\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "guided_backprop\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xD1hEpJHzF/PfkbWDkZ1G2o2WBTteguSPIH4oDHkSnJF16HwXlGdDq0Eh7/8iTcVPcfpnU6PpnyIijZsCQs3IwQI7M6fcY1cIKq4ohvB4KNoHnpLAFwKvBW7A5XBhjAkGhGrJEPJ/YYkOiyY2LBaAnJIcAGLDYwMZQiXuUvtLS+566HQJOELrCPnPgG86tEkBIRFp9ryWN3B5a+bW4D9W3GwHgzzFkL0K0hfApLVgjjDp1/LCuodh2z/BuCCmK0xaB0a13aRp+SlN//zFp7/gw40fsu2ubfRK6FW/nVsWWJ7gKq4nkKZ/iog0bpoy1owcLAydqlXhrYCI1lCwM6S9xHfs5J/+FRMeA9SeIeQvNh0dFk1shB0Qyi2z6xLFhscGMoRK3aXgirVvz1O9r/SidAA2Z2w+6rGJiDQ1+/P2By6vTF1pXzg41w4GxXSFwU9Cu7OhcGfNwaDERDCm+s91cbDtX3YAyBUL+dsUDBI5zjakbwBgffr6o79yba/lxEQ4MAs+TYAPw2HBeXbAt4m77avbFIgSEaknCgg1I1WXh/daXohsBwXbwRUdaC/yHUuEOezl4f1ZP7VlCJVU2NPDosOiaRHeAoC8Unvlstjw2EBgqdRdihWVCNmrg18+fEvel1c6gbc8dXldhygi0igVFcE998DAgXDddZCRAduztgf+vyxlmX1h/V8grg9MXAF9fwdnTYNu19fcaXp69bZIYEIRtBkFlx+ES/fC8H/X+3hEmrMtW+C112D2bDt5B2BTxiYA1hxcE7pxyUHY9ylkLAs0fbjhQ8wUY59Ig5pfywCd02HxleAugogEOLTwyDMFG7E5u+YAUOGpaOA9ERFp/Jr+p4YE1JThUx7RwU4zLssOtBX7Dl4inBFAMEOotmXn/fWCIpwRxEXEAcGAUHRYNMYYwhxhWFh4ozpD/hbw+ha2N3Z6847yYH8L9iyo4wjh+eXPM3vn7DpfX0TkRMvNhb594T//ge3bYdo06NULtmbY02jbRreloLwAy11iTxEb8SK4Yuwvfq4YGPosANM2TaPlEy1//MaGAq44GPMZhLUAZwR0u+44j1Ck+ZgyBYYNg/vug5/9DMaOhXK3O/D/2bsqHaPsfAO+7AnLfw3fngsLzgfLGzgx9sPBH+zt2revfkNO4DYD8YPg8gNweSqc8eHxG9hPiL8u1IZDGxp4T0REGj8FhJoRf4BnTNcxRLrsAqT5zlbgCIfUL8HrAa87kCEU7rIze/xTvvLL8qv1WeYuw2t5CXeGY4wJBIT82/qvG+a0s43Ko0+yr3hgph2IKk0DYJMvINQ6qjWZxZl1Xmns3tn3ctEHF9XpuiIiDeG+++wsgm++geJiWLsW2rWDzZn2l52eCT0ByD74LUR2tAtJV84C8E0ReWPtG+SX53Oo6JDdXtOXyL5A72uwv036+LJAReTYfPYZPP00XHklzJsHf/0rbNgAu3Ls4K7TOFl9cLW98aHFsPouaHcGnP4/6PdHyFoOxkHS3iQAvtv3nb1tWlow1Qjsy/tmQkJ7OPtrOzvIEQ4dJp7A0davp797mmGvDjvsdh6vJ3B5RcqK47lLIiLNggJCTUB6OjzyCFxzDUydCoWFNW/nsewP0Xk3zAsUNMx2xtoHFskfgbcUvGUUVckQ8geP/HWBKvMXlPZPC/MXoM4rszOE/AWl/X2VRiSCIwLW/hGy19jFUYH1ZXZ/fdr0ASAlPyVwG0NeGcLjix4//P1QaKdUu73ukPbtWds5+Z8nH1mQKW8LrLwdki6CbS+AL/tJROR4KCqC99+Ht96CUaPA4YDevWHWLPhm5zcA7M3Za//e8wXED7SLSVfmsrM4l+5fChD4Mlnjl8jL+kCH8yAs5jiOSqR5uuceuP12ePllGD3avvzNN8G6QT1b28Hd/LJ8WHUXdL4EzpoOnS+GPvfB+PkArEtfB8DHmz6u/cbSk+zXcuVC0pWm/zc2kxdO5oe0H6odw1VVuc7kvN3zjvduiYg0eQoINQGnnGIfcDid8Oqr9heMqryWl8LyQpzGSZgzjPjIeAAOeMLsFcbS58Pe92H3WxR5wQARrtCAUH5p9QwhfyaQP+DjDwgVlttRKX+GULgzHIOhIKyNPUWheD/MHgHp3wLwqR1XIjnXTgOev9s+KCqpKGFt+lomJ00+7P1QeapZ5YDSf1b/h715e4Nn5Wqz/WX4ZhgU7rZrK23/F7gLDnu7IiJ1tWQJ9OwJp58O4b6V3x0OaNMmOC3iQOEBAHakrYKoxMBU2wCHiwpPRSAQ/8XWL2q/wbJMaDWg3sch0tzt3Qs5OXZWUKwv6S4qyp4O6s9k6dKyCwBbUpZA/la7fpcvoIsrGuJ6kZqfCtjHVasPrq79ZFb6t9DhHAiLO57DOiEsywqUH1i2f9mPbrsiNZgV9MW2H3mvExGRI6KAUCOW7Sv7849/wMKF9hmpNWtqniWQXWJv3CLCLvqcEJUAQFp5MfiCNqy8DVbdFZgy5g8EBQJC5dUDQgVlBRhMYBt/QKjEXRLS7g8u5YW1CT1j7bPdd0IotcA+EJq+dToQPPvjsTyUe8qrXa+yz7d9HrjsDygBvLfhPQA+2PBB7VfO2wxrfw9nz7DP1g37F1ywzg4MiYjUgdfy4nzMySurXql1m1Wr7Hoj7ionxT3OQixC3yvTCqsUlp2eCO8beN+w6q2IQPP7G9+vfaeqLk0d6KOWVcZqWt0oMbH2/kWaqSVL7Cy/qq/l6Gh4c92bACQlJ9nbrnkaWg2xM6Yrc8UEMv0Gth8IwK6cXTXfYHkOtOhZb/vfkDYe2hi4/OW2L4P/cJfA/umwdSokfwzuosDJv1PbnIrH8gQXTMlYCkt+ATMHQtKFkPJVjcebIiISSgGhRuyTT+zfN9xgH3DExdlno2rirykRG26ftmoT0waAjKIMiDklZFv/lDF/dk+kKxKDobCs+lw0/5Qx/9Sw6LBoDIYyd1lIe6QzEmMM2Y5W1aY7FHqh6iKp69LsdOm31r0VaPuxYtOWZYWkVvvPGuWW5nKw8CAAzy1/rvYzbTteha7XQJuRdqHVsNhGnXotIg1vwZ4FeC0vf13011q3KS6G+HgICwtt35pt1w9yGEfgvfhAaQGUpIPl+8ZZGgwQfeN74x6SOAQIXbI+hCMSyrKCfwf6qGWp6ppWN6ptxSORZmbzoc38ad6fADs7qEMHO2ZamdMZXOXVf2IrLXszxHS2A7SVGQevrn4VCJ4g+9/6/9V848YBladXnYjgrrvIDtCs+T2sewTS7JNvlmXx3x/+S25J9dICR+LrHV8HLr/2w2v2hQOz4bNOcGAOuAvtFdTchUzbPA2ALVlbAHh77duw8W+w9AbodAmc/h70vBM2PgYVedw/+37MFKMVyUREaqGAUCPhLzg6cSKcdZb989hj9v9qCwJVllGUARAo+tw+xk4jOlR0yK5JUYk/Q8if7RMVFoUxpsZVyvLL8jHGBL6w+FcVc3vdIe2RYb4sI08FhMeH9LHNl/gT7gwPpFMXu+2g0fQt0wP1id5Y84a9YQ0HNYuGtgbA4XtKf7b1M7xeb2DqhP9MW+W55yHS5kLXq8BVpbiqZUHqDPh2EswdY/+svB3SFsB3V8Ps0XbbrOH2AYm7hvl6ItIs/eXbvwBwoOBAsNBzFTEx9hfJ8ioJkGkFmQCc0+0c5txgL7G81RMOeevB6XvTjwymg071fQ/z1yp5esnTNe9Uy1Pt2iP+wqyRNaSUVlZTymlNbSLN0CUfXsITS55gRcoKIiLsGo7eKrFV//GXwRDjq92VUZIH7iq1wHwBnUV77CDLgQJ7qug7696p+cajO0PW98HXsj+4e6e3euDHGMjIOLbBZn0PX/a2ay12vgQSJ8D+TwGYv2c+t351K/d8c09we8uC3I1w4Bs7uJO/rdauX1j5AgBtotqQX5bPjv0LYem1cM48GPoPOPVhGPwUaW6LCq8d2PH6iul/v+0D2DYVJi6DLlfaU2I7nA/nfIsV1pKpy6cC8NGmj45t/CIiTVS9BISMMecbY7YZY3YaY/5QH302a4V7YO2fYOkvYdmNsOQ67rp+A2++afH++3ZgaO5c6NHD3rzq2aiaZBRnYDCB2kH+3/vz90P7cSGBEP+y84HsHl+GUE3LzheU2RlC/uCRP0PI7XXjwBHMHHJVqi3UbmxIHzuNvS+X9r6UHb+1V+LwL1sPwQ/9TzZ/Qrm75mljr/Sz5557K53lnrFjBg/OfRCALRn2maS7Z91d4/WxrNC6HP4zbR84YN+n9gog42fbBycJp8OKW6Hvg3DOfDj7KzhvMfS51/6ilrcFDn0HmcuhOKXm2xORn7TPP4frroMbb4Rf/tL+PXfukc9A2JOzh2UpywLB92eWPFPjdiNG2NPGqmYIbUqzvzwN6jCI0Z1HAzA/NwurOBVy19sri12RBtda5HmgwLdf/sUDPtv6Wc07ljgeDnxt144Duw+AO6n5SyRUL0ydlnZkd4JIY1RRYNcSLNhpT8uqxe6c3YHpXDd9cRODBsHq1dVP0q3eZx9/DE4czPJb7OXkV5dUYGWtCgZ3fYo84M9j8R9X+QND1XSYaNd/9GddHy6427Ztja/l5GR47TV44gl45hn46CMoqFo+0VMOCy+BMdPs4tdtz4D2Z8NgO/B85cdXAvDO+nfsbKjsH+wTZfum2aufGSfsej00O9EnJT8lMMbMEjsQvn7ZvXDSlRDX287YNg4Ii2VlykoAXA4XLcLtEggDynZAx0vs+9LhL8TmBGcEc3bNCdzOr7/4deB4UkREgo45IGSMcQIvAhcA/YBfGGP6HWu/jZ1lWXVbOv3AN7D0Ouh5B4x8GYY9z572r/PxnP68844hIcE+oxwZCRdeaF+lpiLSVWUUZWBhBQJBLSNbYjD2h3DHSeANptIWWU4sggcjkS57uleNAaHyAizLIibcPuvlzxDyWt6QDKGosCgsy7IDQp0vrRSAMqx3dQWgb5u+hDvDiY+MD5wBAvuD32nsFOgvtn1R48o5e8fZ0yTOPOlMerW2V1Bbc2ANmcX2wYW/htHSlKU130Htx9hnuqpm+ER1gpEvQUTr4MHGnrdgwKMQP8SeVhbe0v7fnndh4UVQuMuuPRQWB6lfQ1l2bQ+LiPzE+N9adu+G//4Xnn8eXnjBrtE2atSRBeABfvX5rwBIL7LP2j+77NkaV885/XTYudOuA1fmW23R44FV++1Mn5NbnYzT4SQ+Mp5ir0V5XD9Y9X/2l0DLAncRq0vt63WM7chtQ28D7CkqNX4GdTjPPtOfsdh+v/N6qm8j0hxlr7Zrz2x/EYpTofSQvQJrLZ/hl314GQAtI1qyJXMLm5zvkJlprxDofy0DrPetGDas4zD6t+tPu5h2bCjz4vWWwf5p4PG9gK9IY7XvnNegdoPYdMcmIpwRlHnKgnVyKms/3p5GVbgbPGXB4O5LVI9c1xDItSy45RZ4+mn7ePKhh+Dee6FXL6ioOruqcKd90ix+aOh0+jD7WC6/LJ82UXYpgpunXwsLJsCZH0C/h+wgdIcJ9nFTREK1YczdNRewi2i3imhl91e4365v6Tv285+k2zLvUgDuH3U/e+/dC0BRRZV6a/4Teh+G8/P3zwfs48gKbwVPLH6i+v0oP0mWZTF9y3R2ZddSQ6sSr+XlwTkPBqYT+q1NW8tvZ/6WUnfpj92QfRJ3499gw2OwYQqsnwy734byuk2BFGls6iNDaCSw07Ks3ZZllQMfApfWQ7+N1oztM3A85sDxmIOFexfWuI1lwdtvw6OPwiuvwMcfwwcfwP7ZT0KP2yHmJHvlifBWuCIi8XpNtc/3X/zC/r1kiX02x+2uPTjkn67QJtr+wG4V2QpjjN0e2Q5iuwe2LQhvC1QJCGECK0BU5l9lzF+bKDosGodxYGHhMI5qxaaLKoog8ZxgAMrVgu9L7YGdEm/XMuoU1ynkNmZeO5P7Rt0HVFpOuRKP18P6Q/aXp+lXT+fv5/wdgE+22EWWRnYaydJfLyUuIo5Sdym7c3ZXv4N63AZ73rPPCFYUwGX++htVziZNT4RDSeDOD9bx8B98rH8Exs6EThdBXC9o2Q+632wHk0Tk6FneOhUFzciAlSvtIEtSkv177dojC56n+JL67r/fPtPfqhW0bI7NyYgAACAASURBVGnXaWvR88hqcLi9br5P/R6wswJiw2OxsJizc061baOj7QykX//a3leA9eth1uqNGAyd4zoDkBibiMGwMfEqyN0Mc86A7S/AkmuZ63tr/tXgX/Hqxa+SEJVAUUVRzcVoWw+zp40tvBS2PAPJvmL7Vb9EWlaNXyLLy+3pyo88Ytex+/ZbO3PqpZfgrrtg8eLD38ciJ1TxAcj8HjKXQcYyyFwJRfurv7fMPRuGvwB97od2Z0Hb06H7LTV+hm/N2MqGQ3adL//qfg8teIA774Q777RfB243HDwIT79vr4rVr419rtQ/NX5Hm0mw+gHfFM4KKMtise+825ldz6RbfDf6tbWvszxlefVxJQyHuL4w/xxI/QoKdhzV3bJ2rf3affFF6NTJrnUUFgZDhtgrHm7aZL+PrlgBq7eehLcsF8ozg3WL1k/G+14wQu7P7lm8exaWuxRiTw4Gj6YnwsexNdY3mrfHXjzkn+f/kz337gHgzax8rIPzwFseErSe77t/BrQfQOuo1sRFxPFeXjlW6pd2gNsTjMQtLg5mTvqD8c+veP6o7qPCQvjhB/t9bv58WLAAli6FAwfqp151XmleYFXe5sBreVmcvDgwjbJye+WTzgVlBYz4zwiu/PhKerzQo/Zpk9gnP3q+0JNnlz3Lzz/5OX9e8GcA3l3/LkNeHcK/v/83cU/EsSNrR+C2vtr2FasOrLI7+O5qyFhiZ/33vht63wN9H4CTr4fwVvV8D/hYXjvQXJJm1wWsKDj6J5Rl2c93d7GdwXe461uWfbsiNXAdfpPD6gRUrl6ZApx2tJ2smTWfaGsv7U4JLXC8/JNPGHLhhURExwTaPlw8i2m5X1MYBWEOF26vhyEJfbhoVwcGnzee6LjgC/jdd2FdynAe+3sMDn/2O3DXKxPZ1GI7xgUey0ubiFaMsYbwUe7XJHuycGCwsBjTbijXpVxPYq+zadUheOCfs28XMUULGTxhNA6n/QHn9no4Y9oN7LAOBrYb+/ZYroqcyP9FDaLf+HNxuuy7fOeeKO6777TASmFgv1Yz1/8O99YncCaeiwlrARv/xklbnuI3Zz3BFZf/jldeddCxo8EYOLQvDUjkyis8TDjX0L+/g8WLvTx112ckti2jVYeOgb637rSX8YwtKSZnaxLOQ3sxwP7lp/J08louOf1OepqHwIKdJX2ANFyFueRsTaI8ZydYFqeldWftR8/SZeBQ/IdHafvWYWHhKswnZ2sSFQWpWJYXLPB63JTtXUNOVjphpUVYWHzyTCIdLoGJ0cNp6V2K2+NkdVoqDgwJ+TnkbE2iS1hL/OtNJITFMbjM4I3pzj+ATYuWs/zgFHqOPhP/eaZFy/9LcUUxrVyxuPZvokdJBQZIOZgMLrgoYQidc7IY02YQM1IXM3PqFC4cP5FWicH7Z8eeKPJ2XMU49xicXS7AxHQDYNvuWHosuQnniOcDB4X7MzvR5vvJRLjicXS7KtBHUUE5URnLccQPsA/uNj3JlnlfMOWbN/jdU8NDntdzP1hJROsODB6REMg4SN2bxz//Hcf7n8TZKefGfrI+//Hv+Kz8E/IowOFwEO5wcVnCeFbuX8YPxv726rY89I7ryiPOGxiQGE/nAUMwvo4LCzw8+Ptwbv/DUCKjggdjCdt/Se/TR0Hf+wNtXo+XRVPv5tTx43BFB8/kla56nA7DJ9kHyj6FhXD5hG088kRX2rYPznfZv3Y5FelrGHluP1yuYMx5+fSvGDDudGLig/1mrJ9N9/a7cJwZ+mF/0zPn8rV3PWWWGy8W7SLj+VnUuczImcM2dxouhwvLsjiz3WC6ZsQwJ2wjpZbbzoILb8FdLa9lWLGDfuMnBF5zAN/PXMQp/U8iocvJgba0fdmkbVrKgHPPD9nWWn4zrc9/zQ5g+mRtXUarnXfgmLgInNGBdJFl/3uT9vHZtO4+3E5tB/Kyitm2cCZDL74UV6X5QOsWrSchwaLTqQMx2Ncvy06mddrfCb9wacgZ1LSPLiVx8Dn2QYpPQVEB10wdyZqwQ1gYKiw3PWI7c0bFAD4unkOqOxuXceEwDi7ufBY37e/JkAsvJDImOC106w+7cVUc5JQRo3E47P21LIsbPr6HRc6dVFhuPJaXTtHt+EPMTQwJD6fH6DNxOILPnxXTZ3Dq2acRm9A20LZncyqlGdvofebZgfdFgAenTWa2axPlePBiv9/e1eIaBpc56DP2HJz+bT2FxK+/DK7MsjPvfPflvs27SV81jR4jR4Y8T5IXvMYpo8cT3Tn4+nrksRYUWN35979DNqXg/R7EdHwTYs4KtGWkHGLbtMn0PWcSDt+Z7hjfMdU/nnZz190uSkvth7k4N4eE4iLCQ7tlRfeeRH0ylc79Bwdec1/sXkSJp4QesSex4OypvLnzSx5YPZXP332ZjqdupsugoRjf2LA8PHzOW8yd9RKXXx5NaamT8DAP3gd34MAQl5VGztYkEl0xbMHioad68/rF53FS3hycq+/BQySf5EYAZfS1osjZmkS/Fl1YXJLFwqmPEXnhZXT27WvO1iTWbopl44qH+M3gW3FueBwHHixg0bZz6bXqKzpU2nbvljSK0zbSZ8y4wPtt0vQfeOedoezc6dt9X9xo9Kk72NlpOZZ7KKsXOLAsgzEWKSu+ZOCoDrTq0CVwn61aaZ8Q2L3L63+IAUibcQ99Rg+mRbcRgbb0/dns/vZj+p57Kc6w4IpML/+7mLiuAxk6yj7JgYHM1Fz++WwJTzzfifBwE8gZcK26mX5nDMHZ+xZ7Q2DHlmKuu87Bh5+1Jr518ITLs49uw9miE9dcF8yCyC/K4cGvxrM5ei8WFhVeNz3jujDePYSPSuZwwJ2DyzhxGAe/6jySZ8LSaXHJTIjp6uvBYt2cbwnLSaLD4AkY3ypv9mvubha7duO2PHgsL52j2/HzsAksK1lBVkQ54Y4wPJaXofF9yXvjIW66ZyCJHYP3w8JZ+1m5vj3PTI0OfJaUlsLF52Xx+v/aMHhwMKstZfUCTPL7dJv4WxyVUt0Wf/App/TvQlzHHoG2efO8vPDh6cyYGRGSFTfvtbfp3C2K7v3aB9rTU3I5uP47BpwX+h56zU3tuf6O3lxxpfF/nJG7YwmJ+/8P5/lLAtklAMvee4N2cRm07jE8kBWSn13C1m9nMvSSS0LeQx//exhteg7i6muj/Q8npYXFrHzrGc67YQxRUVWCDqXp0Cb4nHLnJVM64wJiJn0OUcHjgFwzhLgD83F2v97ODNj0JGx5iqQt4+l/1e9xhgVf/XfMeMD+3fNKbut1JePm3k5mcSY/GzyJGdP/x6WXtMTpclJa6sV113cYDO2L7eOv7uGtWQXc9eYEPjx3E/ELf4bT5cTrLuXz3EiglN5Ek7M1iQHRHVnDGra89S69J2Tj/+TK2ZrEjj1RLJn7B+4afjPO767FQQUW8N2OCZy8fHrgdQ+wefZ0Dm1ZRv8JE/G9YmjHIiJNH76ekcD4c5yBrKbnnsoj9VBLXn+98p3YguW7X2b47LE4T3sJ02YU9LmXNxf8Ayji5JiOfDjmCf6+4U2+SEni84NDuPi7m3Ce9i+MMwIsi/ziWD5YcSNndlmL0+EFLCwLvtxorxDbp9yBtXctHaLasDivgFlrenKeNQbnsCcw534HX/VkRYkTg5fOhfZ92TWqHRuyC3jzh0u40YzCOfSvmLGzsIr38bs3rgcKeXro3dzc4zJ6fXE5GUUZzHn5F8S0vZHI+C54fe9Th3btIifHzUmDhgU+5zZtDePZf7dj+/bQp9PWZWswdz+M+XRWoK2IaIYmbOR/s7oxeHBw212rN9H13NOJKgydg3fJbzryVWLoVMDbWl3J/PxF7PIGgyWD43vzfnhreky4G1fHswPthzbMp33ey3DekpA+lrz1Cp0Si2nZdXDg8zMzrYDklfMZNOkiXJVen5Mfc9J92EAmnB98HW7fnMcjj8Ywd0E4LVsG+71i0kEuuaojd9wRbEtNT+XKF85nc9QeLCzcloc+cd1o8+n/uOiK4dxTqZxUZl4mg5/rS6ojM2R/b46+mLeKv8ZT6eTr8Pi+rMrZErLdjZ/fyLZvvuXOZcl0+uLbQHtuJMT/nsD7AMDfFv+NhUtm8J13XaCtwltBr3/3YiCdWU9oKYetXU6mx5mTcIL93rPpCdj8BA998A9GXnsLiZ2Cr/sNO+Yw8+AztDwpjriwaPIriomPaEHvteNo13007bt2tN/oDOQd3Icr/WtGTjoDp8t/jFNC/LpJ8POCkEBz1qq3SCicBmNnhOzbw6/cwUd5n5LjLbNPsjvDeardcK49qQLGzwFn8LPgX1PW4YrrxJARsXYVDAMRWbMYGvU0nLfM90Fg31Hff/418dFZJHQJfi5n5udzx6zfsS7CTiAo97rpHdeF08v682HpbNLdeThw4DQOLoi7lIP//QcvvXkS7doH7/wtCxfizd3GiPG9cDrt519RsYObf9OCPz7Rl/iE4PPvyw/2kHyoI3+ZEhX8jLHg1v+OYUXEetx48FgeOka15cGYGxjsCqfn6LOC37ndhqtvTOChv/aiR09XoI+k1dP497a/kBKWTpjThdfyMrrtQFqlWswL20w5biwgPrwFd8Zdw5ASQ79xod8VHv98Kqui9hAWac9ycRjDZeFnMqo0l57nXh84bgZ47YVMWnYbQPdTO+B/8Asz0ijZ+h5jrjyTsLDg59G09zKgZT96DOwc2N/CnDy++mAnP7tlMK4wZ+Czcl3SWkxkAgNP6xTYtqyogNyVLzPuF+cQFh4awlk3dxEjfvkAMS1jqIv6CAgdEWPMbcBtAF0qPQH92q25hk7XvwJdxtkN6yfDxilcsBb4y8sh277zSBzLnflQaQbTiqyNvLweeOSFkG3Thz5O1OixdApNOuEz59yQ6+8s2E9UeTbJ4fb8Zq/vUHLhoR9Y+PfV1QKvSS/8h7EbPoN7Hwm0WcCOyTAiBZ6bDeVO+O0k6OgsZkz+Z3BPsMjncODd0ZN4YuiNRJ81nKiEKCyPl0VJA7k0byhXndoZRvWFmFgIc/A35x9Z/s3n/L7r3RyI6oHDXc4VfT4AXmRZyRD+9uXDzP6yOyNZyYhb78Y1YifEdrPPXrsLGXx/CzaXGH72x4+JT/6Y0TEw6mqY8F5HHnIPsd8jbgY8MH7TOraMg8senU78zukMioDTr4FnBmbQ96xToeM4u6iou5DTMz5iYAlcMzWJ+FuT6BsGY66B8LgIKvLL6H36dUS54Zp+sHsMnDt9K1dP/wW0A26BsK9yGH1xGwppxZkT76NVKdzaG3ZOgO3AH77OJ/7hcZztgEnXwC8Toxk10gunnhPYh647fsPQYrhseSHxfx5HK+Dyq6Cka2uyvBY3/PpV4nNf5cGusP8CuOusd2Ds28Ez+3N+w4K3S+mxMRFXRT5c/BG0AjrCBw/+gsn9HoNzP4LEGCgpY/a+G4ld0ZZrel0P/W+A2AjoAM/fcSd/Gj0aLugFnU8Bt5vk9C5sTB/NsNB4EMNHnEFqKnQMHouy4ptdFJR1oWev0G3/k/sceVVeqTMPLWZ3bCqVs6S35Sdz0Yp/0nJW6Nmm0thEvrYO8tF5oX0s+e82evc7CUp8Bx9bnsazcSpjl3ngdy+GbPvKpNv4dYedhCestw8ydr5E+foPWfhDNnPHhfab8/W/OH1vNtz/25D2C64AHn4upG3+BZfT608R4LQzyfyv+7fLQjZjT2EJqzKT2BJpB1vLfRlm36b7zvBU2j6nPJ/klBXcl3sI7nkqpJ/zrgAeCO178xmnM+7kFLj/2ZD2Hb/rQevT90N8NmBg85Ns/yKJ0SO99lLBDldgf0//HJge2m9q935MHJYMD4felyMviiR6Rmj68r5OneG5yODZG1+/yfMHkNj9EJTlBPYhZ8WLzPQUQqWZPllleZC7jZRW9lnQCssNFkzbN59P1s+HR0OXPXc+OJwRpe3gV38OaZ85mZB+k4sO0jIjidMKIuCWySHbnn8F8HDomNddN4ax0WXwm9BVtd6o0m9WWR4H9y/nrrxsuKtSCn8YrLtlEIO6vwMdhoLDAXv+y97ZOxiz/yDc+PuQfqP+E0Fk90ftOhO+++2ajp/xxYKHefORYbTqEocz3EF5kZvemyMZcOoaiOwCWLDtn6TMSeLMAQUw4KXAeyX/stOIev3+Cm79282YHqdgvF5aFS7h+ddd8HMrZNuTcnfQcdxoaDMq0N7i4XAGlsAfP9lP/IPjuD4GPrsKHk9ZQrvHQw/4CIP4i2FL6jSm8CjzOYdB5WvZ73LhrmjNiLG3E1MBt50KWWfBWZ9spNsnn9mTtEeBM7mU/u3a0tFRwQWXPEJ8CdzfG/LGwc1nvQuj3gkUgY4vT+KDFztyjfsAYa/nwi+BlmC+hrPvmwvD54Rsu251EmMv6RvyfnuuZxjPt5vE44NvJPrMYUS1jsLr9vLNrEP0GHsDU6eGDm/Yi/3gT6FthX0vhavhlO6hScptnG/R4pRF0Hpg4PFMnTGTs7YmwwOhxwFTww8xZ3lbhgwJtq2el87uQ4M4bXTo7W37cCmuCWfbK0j6+i2bOY2Mgo107xG67fKPk/nl0304tX+wbceubJaGbQgWdwHW5WwnPG83B1rarzm35QHLw6wDS/hX10T7hI4zPPBadu8exuBDHeC60PekmZOBSjMJdxemssT9Nd+5MqHS28T67J2w8AumJ4Xu7/x3NxEZ252uXYNtJSWQn+ambVtDbKV1Enb/sJELhuVCu8GB+4GNUxi3ELiHUAN+htVyPHFxoc2XL70JHgxt2zJqNON67IcHQt9D8+KXEdfSQUyl49ONKYYOxgpGqXz7cNo0g+Pz0AOttG69mThiH/w5NLq7us1cbjkvllOCic2kbMviV8mPwdAq45gMfDIZBoyB2DZw6HNK980hMjYOE9HazmDx7cML//dL/tLrNpg4BfoMsOvQeJyM7bsABswPed23LWrPGeWt+Mvtn5JY+CkvDIBnzoDT1s9i3e5EnuIhvuISurOTEmcFmeUtmHDpn4kvgltOgS3nwZn/20+b9zfYr+XTwJEJPcxJOGOyufTaZ4jPe4Zbu8C6SXDfWe/D2PegvX1SJr48yXfcEknYG3lwA9AWzGI466Z5MGqu/ZpNT4eEGLKWTGXs2Cjo/0zgtdxpy9msGxzBRxdfzW863UBk987grqB10Rw6tT2Z9/42hNgOLXCFOygtcrPhNzsZ1WkHnH0u9AXckGe6AEW89MoBRv7uRp5pDQevgHVvnM3lPZ+A8z6EUzqAx0U2rbn9iiVw5r8Dj71nwxTGZDkhE8547HbCvPDQKPhvz3asfHcwk/p9AeMnQSJYnWBMppOyXA+jHr+dSDfcPQReGO5i/3+64uqeDOf+EjqDyYKOLRMYUxHOr6/+F61L/sUzg+GJHoO5+fUP2F9lEcaJf6zg8l8PYsz5wbb+IyH7rcm8etcVxPdpR2SLMLwVXsz2F7n0zwNh2szgc+Jpi9K7HERHm5B6cGkrP6ZPePWpwv5gULcc8BjY3wq2H1jLrujQzJm1OdvoGB9HWHx3iO4QuN/Wf76Ycad7cZVl28cjm/4Om//OGV9R7Vhk56BhTOiXDL8PzY6aH7eBi25qSb9Tg23FB1Mpcw8gISG0rl3sjoVUlF1FRUXwPXv7zm2sCNsY8v61NmcbN+1bSUH2AAryw4PB6JQsUh2ZxJXC8AOQHgub2sG21G/wxHtpVQIxFXAwlkAw6LfL4c+LYF9LuOJqODt2I52evgk+XxC4371Ti2A3/N9yeGYepMfApOsgKj6NeKeTr9/wMCgdXhsC902C9aQQWwbj9kJBOCSdDGe+9BvmffsVg/o+C30G2wO3wnlmxgNYX4Xel28sf4EZniWwt8oD+tRH1b4nLnp9GmM2vwu/fzTY6ISl14zi9DZPQ9eh9ntMyvusm5fG4B5htD45CSJawO7XYO9r/L2GBT2fW53G2bmt6dThM4iOh31vwf63ee7p3fxvdhtGnxncdtuK7qQuCafTyV9BdBvY+zokv06f92JoMTM0ZXrPSW2Yf3NmyPH091mbKS/YRnoL+wDOixev5eWbfSuJOdCN4SNCuiA7fQqD98fD3ZUih7RigSuH2eeEbvvuk1uI69iTqiGB2c4lIc+pXYUpRKQtZlSBgVunBNrLCeNbyvnsDEKCl49+8k/WWtugUsnZz/cn2RcqjS2jLId9+5dzT24G3B36ufzc5NBtAdpkhXHnxQa6j7UbfJ8b90+1yMyEhEqzYVd+toWRO16CgZND+rgdD7v3OOjWLdi2bGYyX38/nlerlH289MIhLF0TR/9KxyJ71mdw8nv/gEFPhm58fVfGvruXY2HqVOemcgfGjAYmW5Y10ff3HwEsy6p1ou7w4cOtVatWHdPtis2Yw2QJ1lQUtJE47NjqKjGx5mWT27dXsVSR5mbyZJgyJaTJTAbr0SN889H7CWCvrJSTY2cOejz23w4HdO1qT0WpykwxR34fi9RRt392IzkvuaF3o0nr2rJroJ7PYXnKfLXLfFNcDHYxbOM47FXrg2XBnj2Qmxusk2SM/R41aBC4TthpchGR48sYs9qyrOGH37J+AkIu7GSOc4BU4HvgWsuyNtV2HQWE6o8CQnWgL3Ai8iP0JfL4O6ovkSIiIiJyxI4mIHTMsXDLstzGmLuA2YATeOPHgkEiDU5BHxH5EQpUiIiIiEhzUC/JkZZlzQRm1kdfIiIiIiIiIiJyfJ2YSbsiIiIiIiIiIvKToYCQiIiIiIiIiEgzo4CQiIiIiIiIiEgzo4CQiIiIiIiIiEgzo4CQiIiIiIiIiEgzo4CQiIiIiIiIiEgzo4CQiIiIiIiIiEgzo4CQiIiIiIiIiEgzo4CQiIiIiIiIiEgzo4BQU5SYCMbYP37G2O0iIiIiIiIi0uwpINQUpacfXbuIiIiIiIiINCsKCImIiIiIiIiINDMKCImIiIiIiIiINDOuht4BOTZdu4aWCrJZtV+h2rY/XV27NvQeiIiIiIiIiDRNCgg1cnv3NvQeiIiIiIiIiEhjoyljIiIiIiIiIiLNjAJCIiIiIiIiIiLNjAJCIiIiIiIiIiLNzDEFhIwxPzfGbDLGeI0xw+trp0RERERERERE5Pg51gyhjcAVwKJ62BcRERERERERETkBjmmVMcuytgCY6uuei4iIiIiIiIjIT5RqCImIiIiIiIiINDOHzRAyxswDEmv418OWZX1xpDdkjLkNuA2gS5cuR7yDIiIiIiIiIiJSvw4bELIsa0J93JBlWa8BrwEMHz7cqo8+RURERERERETk6GnKmIiIiIiIiIhIM3Osy85fboxJAUYDXxtjZtfPbomIiIiIiIiIyPFiLOvEz94yxmQAyVWa2wCZJ3xnTpymPD6NrXFqymODpj0+ja1xaspjg6Y9Po2tcWrKY4OmPT6NrXFqymODpj0+ja1xqm1sXS3LanskHTRIQKgmxphVlmUNb+j9OF6a8vg0tsapKY8Nmvb4NLbGqSmPDZr2+DS2xqkpjw2a9vg0tsapKY8Nmvb4NLbGqT7GphpCIiIiIiIiIiLNjAJCIiIiIiIiIiLNzE8pIPRaQ+/AcdaUx6exNU5NeWzQtMensTVOTXls0LTHp7E1Tk15bNC0x6exNU5NeWzQtMensTVOxzy2n0wNIREREREREREROTF+ShlCIiIiIiIiIiJyAvwkAkLGmPONMduMMTuNMX9o6P05VsaYN4wxh4wxGyu1tTbGzDXG7PD9jm/IfawLY8xJxphvjTGbjTGbjDH3+Nob/dgAjDGRxpiVxph1vvFN8bWfbIxZ4Xt+fmSMCW/ofa0rY4zTGLPGGDPD93eTGJsxZq8xZoMxZq0xZpWvrak8L1sZY6YZY7YaY7YYY0Y3obH19j1m/p98Y8y9TWh89/neSzYaYz7wvcc0ldfcPb5xbTLG3Otra7SP29F8bhvbv3yP4XpjzNCG2/PDq2VsP/c9dl5jzPAq2//RN7ZtxpiJJ36Pj1wtY3vG93653hjzmTGmVaX/Nfax/dU3rrXGmDnGmI6+9kb1nISax1fpfw8YYyxjTBvf341qfLU8dpONMamVPu8mVfpfo35e+tp/63vdbTLGPF2pvVGPzfc57X/M9hpj1lb6X6MZG9Q6vsHGmOW+8a0yxoz0tTeF19wgY8wyY38/+MoYE1fpf43msTNH+f27To+dZVkN+gM4gV3AKUA4sA7o19D7dYxjGgMMBTZWansa+IPv8h+Apxp6P+swrg7AUN/lFsB2oF9TGJtv3w0Q67scBqwARgEfA9f42l8B7mjofT2GMd4PvA/M8P3dJMYG7AXaVGlrKs/Lt4FbfJfDgVZNZWxVxukE0oCuTWF8QCdgDxDl+/tj4FdN4TUH9Ac2AtGAC5gH9GjMj9vRfG4Dk4BZvs+MUcCKht7/OoytL9AbSAKGV2rvh30cFgGcjH185mzoMRzl2M4DXL7LT1V63JrC2OIqXb4beKUxPidrG5+v/SRgNpDs/1xvbOOr5bGbDDxYw7ZN4Xk5zvc5EOH7u11TGVuV/z8L/KUxju1HHrs5wAW+y5OApEqXG/tr7nvgbN/lXwN/bYyPHUf5/bsuj91PIUNoJLDTsqzdlmWVAx8ClzbwPh0Ty7IWAdlVmi/F/mKH7/dlJ3Sn6oFlWQcty/rBd7kA2IL9pafRjw3AshX6/gzz/VjAeGCar73Rjs8Y0xm4EPiv729DExlbLRr989IY0xL7Q+51AMuyyi3LyqUJjK0G5wC7LMtKpumMzwVEGWNc2MGTgzSN11xf7AOMYsuy3MBC4Aoa8eN2lJ/blwLv+D4zlgOtjDEdTsyeHr2axmZZ1hbLsrbVsPmlwIeWZZVZlrUH2Il9nPaTVMvY5vielwDLgc6+y01hbPmV/ozBPkaBRvachFpfcwBTgYcIjg0a2fh+ZGw1afTPS+AO4EnLssp82xzytTeFsQGBY+arUQvwmgAABh9JREFUgA98TY1qbFDr+CzAnznTEjjgu9wUXnO9+P/27i7EqioM4/j/DVPSItLSSg1NtK5Ck8JILbRCQxT7AEOoMIg+hPSiCzOCLoIgi7oIg7ICs+hDs+mm7EOqK60xR8e0UhKdKUeL8qKgTN8u3nU8m0OnmnGmc/Y6zw+GOWfvM7Ae1tpn1l57rb3h0/T6A+Dm9LpUddeH8+9e110zDAiNBg4W3nelbbkZ5e4/pNeHgFGNLMypMrNxwBRiFk022SyWVG0HDhNfHvuAXwqdyzK3z6eJTtaJ9H4E+WRzYJOZtZvZ3WlbDu1yPHAEeMliqd8LZjaMPLLVWkS1o1X6fO7eDawCDhADQUeBdvI45jqBGWY2wsyGElejxpJBvdWolyfnfktu2ZYQV0ohk2xm9piZHQQWA4+kzblkWwB0u3tHza4s8gFL0xKOF626pDaHbJOI/wlbzOwTM7sibc8hW8UMoMfdv03vc8m2DHgifaesAlak7Tnk20V1ksmtRD8FSpztP55/9zpfMwwItRyP+VylfbybmZ0JrAeW1VytKn02dz/u7pOJK4pXApc2uEj9wszmAYfdvb3RZRkg0939cmAucL+ZzSzuLHG7HERMgV3t7lOAX4lpoSeVONtJFvfRmQ+8WbuvrPlSZ38BMah3IXE1f05DC9VP3H03sRRnE/AesB04XvOZUtZbPbnlaQVmthL4E1jX6LL0J3df6e5jiVxLG12e/pIGlx+iOsiVm9XABGAycZHgycYWp18NAoYTy1MeBN5IM2pychvVi1Y5uRdYnr5TlpNmpGdiCXCfmbUTS63+aHB5TslAnn83w4BQN9URO4gT8e4GlWUg9VSma6Xfh//l803JzE4nGuM6d9+QNmeRrSgty9kMXEVMtRuUdpW1fV4NzDez/cSyzFnAM+SRrTIbozJN+W1iMC+HdtkFdLn7lvT+LWKAKIdsRXOBbe7ek97nkO864Dt3P+Lux4ANxHGYyzG3xt2nuvtM4GdiTXsO9VZUL0/O/ZYsspnZncA8YHHqKEMm2QrWUV0CkUO2CcQAekfqq4wBtpnZ+WSQz9170kXHE8DzVJeolD4b0VfZkJaobCVmop9LHtlI/7NvAl4vbM4iG3AH0T+BuCiXTbt09z3ufoO7TyUG8/alXaXL1svz717na4YBoc+BiRZPXhlMLBtoa3CZBkIbcdCRfr/TwLL0SRrtXwPsdvenCrtKnw3AzM6z9DQSMzsDuJ5Yp7kZuCV9rJT53H2Fu49x93HEMfaxuy8mg2xmNszMzqq8Jm4o2kkG7dLdDwEHzeyStGk28BUZZKtRe+Uth3wHgGlmNjR9d1bqrvTHHICZjUy/LyI6yq+SR70V1cvTBtyenuQxDThamLZddm3AIjMbYmbjgYnA1gaXqVfMbA6xPHq+u/9W2JVDtomFtwuAPel16duku+9095HuPi71VbqIG6keIoN8NffwWEj0UyCDdglsJG4sjZlNIh6A8SN5ZIO4wLPH3bsK23LJ9j1wTXo9C6gsicvhmKv0U04DHiYe5AElq7s+nH/3vu68Oe6efSNxdXEfsLLR5emHPK8R00GPEf/Q7iLu1/IRcaB9CAxvdDn7kGs6MR1tB7FEYHuqu9JnS/kuA75M+TqpPkngYuKLYi8xej6k0WU9xZzXUn3KWOmzpQwd6WdX5Tsko3Y5GfgitcuNwDm5ZEv5hgE/AWcXtmWRD3iUOGHrBNYST7Qo/TGXsn1GDHB1ALPLXm+9+b9NPLnj2dRn2UnhKV3N+FMn28L0+negB3i/8PmVKdvXpKfPNOtPnWx7ifsnVPopz2WUbX36PtkBvAuMLmObrJevZv9+qk8ZK1W+OnW3NpV9B3HCdkFG7XIw8Epqm9uAWblkS9tfBu75m8+XJts/1N104v6GHcR9aaamz+ZwzD1AjC98AzwOWBnrjl6ef/el7iz9oYiIiIiIiIiItIhmWDImIiIiIiIiIiL/Iw0IiYiIiIiIiIi0GA0IiYiIiIiIiIi0GA0IiYiIiIiIiIi0GA0IiYiIiIiIiIi0GA0IiYiIiIiIiIi0GA0IiYiIiIiIiIi0GA0IiYiIiIiIiIi0mL8APkwukXjWSjIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "integrated_gradients10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rescale_all_layers\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "revealcancel_all_layers\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rescale_conv_revealcancel_fc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rescale_conv_revealcancel_fc_multiref_20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlrqW7Ky8MW3"
      },
      "source": [
        "## Making the scatterplots\n",
        "\n",
        "I haven't ported the code necessary to generate the scatterplots in the DeepLIFT paper in this version of DeepLIFT. However, for a notebook that reproduces the figures in the paper, feel free to look at https://github.com/kundajelab/deeplift/blob/671ee67a03bd5bebf4c405af59eec45d3ca2a288/examples/public/genomics/genomics_simulation.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNC5RvoY8MW3"
      },
      "source": [
        ""
      ],
      "execution_count": 13,
      "outputs": []
    }
  ]
}